Maarten H. van der Vlerk
Department of Operations
University of Groningen
PO Box 800, NL-9700 AV Groningen, The Netherlands
E-mail:
October 8, 2007
One of the sources for this bibliography has been
the list of
Books on Stochastic Programming,
compiled by J. Dupacová,
which was first published in Wets and Ziemba [4033].
Please send additions (preferably in
BibTeX
format) or comments to the
e-mail address mentioned above.
This bibliography can be cited as
Maarten H. van der Vlerk. Stochastic Programming Bibliography.
World Wide Web, http://www.eco.rug.nl/mally/spbib.html,
1996-2007.
The BibTex entry I use is
@MISC{SPB9607,
author = {Maarten H. {van der Vlerk}},
title = {Stochastic Programming Bibliography},
year = {1996-2007},
howpublished = {World Wide Web, \url{http://www.eco.rug.nl/mally/spbib.html}}
}
where the macro \url is defined in the
LATEX
style file
url.sty.
I.N. Kamal Abadi, Nicholas G. Hall, and Chelliah Sriskandarajah.
Minimizing cycle time in a blocking flowshop.
Oper. Res., 48(1):177-180, 2000.
J. Abaffy and E. Allevi.
A modified L-shaped method.
J. Optim. Theory Appl., 123(2):255-270, 2004.
Moncef Abbas and Fatima Bellahcene.
Cutting plane method for multiple objective stochastic integer linear
programming.
European J. Oper. Res., 168(3):967-984, 2006.
N. E. Abboud, M. Y. Jaber, and N. A. Noueihed.
Economic lot sizing with the consideration of random machine
unavailability time.
Comput. Oper. Res., 27(4):335-351, 2000.
P. Abel.
Decisions in stochastic linear programming models under partial
information.
Z. Angew. Math. Mech. 73, No.7-8, T 737-T 738, 1993.
Peter Abel.
Stochastische Optimierung bei partieller Information,
volume 96 of Mathematical Systems in Economics.
Verlagsgruppe Athenäum/Hain/Hanstein, Königstein/Ts., 1984.
Peter Abel.
Stochastic linear programming with recourse under partial
information.
In Probability and Bayesian statistics (Innsbruck, 1986), pages
1-6. Plenum, New York, 1987.
Peter Abel and Reiner Thiel.
Mehrstufige stochastische Produktionsmodelle. Eine
praxisorientierte Darstellung mit programmierten Beispielen.
Schriften zur Quantitativen Wirtschaftsforschung, Bd. 5. Frankfurt am
Main: Rita G. Fischer Verlag., 1981.
Jinane Abounadi, Dimitri P. Bertsekas, and Vivek Borkar.
Stochastic approximation for nonexpansive maps: application to
Q-learning algorithms.
SIAM J. Control Optim., 41(1):1-22 (electronic), 2002.
L.M. Abramov and I.I. Bockareva.
A stochastic programming problem with probabilistic constraints.
Optimal. Planirovanie, 16:3-9, 1970.
G.M. Adamenko.
Solution of extremal problems under conditions of incomplete
information.
Automat. Control Comput. Sci., 14(4):48-55, 1980.
M. Ju. Afanas'ev.
An example of the cycling of a stochastic integer algorithm in a
bilevel multicommodity problem.
In Methods of function analysis in mathematical economics
(Russian), pages 111-114. Izdat. "Nauka", Moscow, 1978.
P. K. Agarwal, B. K. Bhattacharya, and S. Sen.
Improved algorithms for uniform partitions of points.
Algorithmica, 32(4):521-539, 2002.
R.A. Agnew and R.B. Hempley.
Finite statistical games and linear programming.
Naval Res. Logist. Quart. 18, 99-102, 1971.
Saligrama Agnihothri, Uday S. Karmarkar, and Peter Kubat.
Stochastic allocation rules.
Oper. Res. 30, 545-555, 1982.
G.A. Agranovich and L.N. Kanov.
A method of computing the gradient and the Hessian of the quality
criterion in parametric optimization of continuous-discrete stochastic
systems.
J. Math. Sci., 82(3):3412-3415, 1996.
Dynamical systems, No. 13.
Shabbir Ahmed.
Convexity and decomposition of mean-risk stochastic programs.
Math. Program., 106(3, Ser. A):433-446, 2006.
Shabbir Ahmed.
Smooth minimization of two-stage stochastic linear programs.
Optimization Online, http://www.optimization-online.org, 2006.
Shabbir Ahmed, Ulas Cakmak, and Alexander Shapiro.
Coherent risk measures in inventory problems.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
Shabbir Ahmed, Alan J. King, and Gyana Parija.
A multi-stage stochastic integer programming approach for capacity
expansion under uncertainty.
Stochastic Programming E-Print Series, http://www.speps.org,
2001.
Shabbir Ahmed, Alan J. King, and Gyana Parija.
A multi-stage stochastic integer programming approach for capacity
expansion under uncertainty.
Optimization Online, http://www.optimization-online.org, 2001.
Shabbir Ahmed and Alexander Shapiro.
The sample average approximation method for stochastic programs with
integer recourse.
Optimization Online, http://www.optimization-online.org, 2002.
Shabbir Ahmed, Mohit Tawarmalani, and Nikolaos V. Sahinidis.
A finite branch-and-bound algorithm for two-stage stochastic integer
programs.
Math. Program., 100(2, Ser. A):355-377, 2004.
Shabbir Ahmed, Mohit Tawarmalani, and Nikolas V. Sahinidis.
A finite branch and bound algorithm for two-stage stochastic integer
programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Byong-Hun Ahn and Bo-Woo Nam.
Multiperiod optimal power plant mix under demand uncertainty.
J. Oper. Res. Soc. Jap. 31, No.3, 353-370, 1988.
M. Aicardi, G. Casalino, F. Davoli, R. Minciardi, and R. Zoppoli.
A decentralized closed-loop solution to the routing problem in
networks.
Annu. Rev. Autom. Program. 13, Part 2, 9-17, 1986.
Z.Zh. Akhmetzhanova and G.M. Bakan.
Solution of a programming problem with inexactly specified initial
data.
Sov. J. Autom. Inf. Sci. 21, No.2, 55-58 translation from
Avtomatika 1988, No.2, 54-56 (1988)., 1988.
Hisham Al-Mharmah and James M. Calvin.
Optimal random non-adaptive algorithm for global optimization of
Brownian motion.
J. Global Optim., 8(1):81-90, 1996.
Hisham A. Al-Mharmah and James M. Calvin.
Comparison of one-dimensional composite and non-composite passive
algorithms.
J. Global Optim., 15(2):169-180, 1999.
Aureli Alabert i Romero.
On the optimization of hydroelectric power generation with random
water inflows.
Qüestiió, 15(3):307-348, 1991.
Chris M. Alaouze and Peter J. Lloyd.
A generalization of Gurland's theorem, with applications to economic
behavior under uncertainty.
Am. Stat. 40, 70-71, 1986.
Horst Albach.
Capital budgeting and risk management.
In Quant. Wirtsch.-Forsch., W. Krelle zum 60. Geb., 7-24,
1977.
Maria Albareda-Sambola and Elena Fernández.
The stochastic generalised assignment problem with Bernoulli
demands.
Top, 8(2):165-190, 2000.
Maria Albareda-Sambola, Maarten H. van der Vlerk, and Elena Fernández.
Exact solutions to a class of stochastic generalized assignment
problems.
European J. Oper. Res., 173(2):465-487, 2006.
Ya. Alber.
Dynamical processes of stochastic approximation.
Funct. Differ. Equ., 4(3-4):239-256 (1998), 1997.
Ya. I. Al'ber and S.V. Shil'man.
Stochastic programming methods: convergence and nonasymptotic
estimation of the convergence rate.
In Stochastic optimization (Kiev, 1984), volume 81 of
Lecture Notes in Control and Inform. Sci., pages 249-257. Springer, Berlin,
1986.
Susanne Albers, Rolf H. Möhring, Georg Ch. Pflug, and Rüdiger Schultz.
05031 Summary - Algorithms for Optimization with Incomplete
Information.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
V. Albornoz, J. Arrate, and L. Contesse.
Solucion de modelos de dimensionamiento de lotes no capacitados bajo
incertidumbre en las demandas.
Revista del Instituto Chileno de Investigacion Operativa,
6(1-2):52-62, 2001.
V. Albornoz and C. Canales.
Planificacion de la conservacion y explotacion del langostino
colorado usando un modelo de optimizacion estoc stica no-lineal con recurso.
Informacion Tecnologica, 13(4):??, 2002.
V. Albornoz and L. Contesse.
Modelos de optimizacion robusta para un problema de planificacion
agregada de la produccion bajo incertidumbre en las demandas.
Investigacion Operativa, 7(3):1-16, 1999.
A. Albrecht, S.K. Cheung, K.C. Hui, K.S. Leung, and C.K. Wong.
Optimal placements of flexible objects. I. Analytical results for
the unbounded case.
IEEE Trans. Comput., 46(8):890-904, 1997.
A. Albrecht, S.K. Cheung, K.C. Hui, K.S. Leung, and C.K. Wong.
Optimal placements of flexible objects. II. A simulated
annealing approach for the bounded case.
IEEE Trans. Comput., 46(8):905-929, 1997.
S.Christian Albright.
A Markov-decision-chain approach to a stochastic assignment
problem.
Operations Res. 22, 61-64, 1974.
Michael Albritton, Alexander Shapiro, and Mark Spearman.
Finite capacity production planning with random demand and limited
information.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
David Aldous.
Minimization algorithms and random walk on the d-cube.
Ann. Probab., 11(2):403-413, 1983.
I.A. Aleksandrov, V.P. Bulatov, S.B. Ognivtsev, and F.I. Yereshko.
Solution of a stochastic programming problem concerning the
distribution of water resources.
In Stochastic optimization (Kiev, 1984), volume 81 of
Lecture Notes in Control and Inform. Sci., pages 258-264. Springer, Berlin,
1986.
V.M. Aleksandrov, V.I. Sysoev, and V.V. Semeneva.
Stochastische Optimierung von Systemen.
Izv. Akad. Nauk SSSR, Tekh. Kibern. 1968, No. 5, 14-19, 1968.
A. Alessandri and T. Parisini.
Nonlinear modelling of complex large-scale plants using neural
networks and stochastic approximation.
IEEE Transactions on Systems, Man, and Cybernetics - A,
27:750-757, 1997.
David L. J. Alexander, David Bulger, James M. Calvin, H. Edwin Romeijn, and
Ryan L. Sherriff.
Approximate implementations of pure random search in the presence of
noise.
J. Global Optim., 31(4):601-612, 2005.
M. Montaz Ali, Charoenchai Khompatraporn, and Zelda B. Zabinsky.
A numerical evaluation of several stochastic algorithms on selected
continuous global optimization test problems.
J. Global Optim., 31(4):635-672, 2005.
M.M. Ali and C. Storey.
Topographical multilevel single linkage.
J. Global Optim., 5(4):349-358, 1994.
M.M. Ali, A. Törn, and S. Viitanen.
A numerical comparison of some modified controlled random search
algorithms.
J. Global Optim., 11(4):377-385, 1997.
Montaz M. Ali.
A probabilistic hybrid differential evolution algorithm.
In Models and algorithms for global optimization, volume 4 of
Springer Optim. Appl., pages 173-184. Springer, New York, 2007.
F.M. Allen, R.N. Braswell, and P.V. Rao.
Distribution-free approximations for chance constraints.
Operations Res., 22(3):610-621, 1974.
Sira Allende and Carlos Bouza.
Stochastic programming approaches to the estimation of the mean in
stratified population.
Investigación Oper., 14(2-3):109-118, 1993.
Workshop on Stochastic Optimization: the State of the Art (Havana,
1992).
Sira Allende and Carlos Bouza.
Random demands: optimum lot size and the newsboy problem.
Investigación Oper., 23(3):124-129, 2002.
A. Alonso-Ayuso, L. F. Escudero, C. Pizarro, H. E. Romeijn, and
D. Romero Morales.
On solving the multi-period single-sourcing problem under
uncertainty.
Comput. Manag. Sci., 3(1):29-53, 2006.
Mahmoud H. Alrefaei.
Stochastic optimization using the standard clock simulation.
Int. J. Appl. Math., 8(3):317-333, 2002.
Mahmoud H. Alrefaei and Ameen J. Alawneh.
Solution quality of random search methods for discrete stochastic
optimization.
Math. Comput. Simulation, 68(2):115-125, 2005.
Mahmoud H. Alrefaei and Mohammad Almomani.
Subset selection of best simulated systems.
J. Franklin Inst., 344(5):495-506, 2007.
Mahmoud H. Alrefaei and Sigrún Andradóttir.
A simulated annealing algorithm with constant temperature for
discrete stochastic optimization.
Management Science, 45:748-764, 1999.
Mahmoud H. Alrefaei and Sigrún Andradóttir.
A modification of the stochastic ruler method for discrete stochastic
optimization.
European J. Oper. Res., 133(1):160-182, 2001.
Mahmoud H. Alrefaei and Sigrún Andradóttir.
Discrete stochastic optimization using variants of the stochastic
ruler method.
Naval Res. Logist., 52(4):344-360, 2005.
M.H. Alrefaei and S. Andradóttir.
A new search algorithm for discrete stochastic optimization.
Proceedings of the 1995 Winter Simulation Conference 236-241,
1995.
M.H. Alrefaei and S. Andradóttir.
Discrete stochastic optimization via a modification of the stochastic
ruler method.
Proceedings of the 1996 Winter Simulation Conference 406-411,
1996.
A.Z. Al'terman.
On a realization of random search for the construction of a
separating metric.
Probl. Sluchajnogo Poiska 7, 307-313, 1978.
A. Altman, M. Amann, G. Klaassen, A. Ruszczy\'nski, and W. Schöpp.
Cost-effective sulphur emission under uncertainty.
European Journal of Operational Research, 90:395-412, 1996.
Adel A. Aly and John A. White.
Probabilistic formulations of the multifacility Weber problem.
Naval Res. Logist. Quart., 25(3):531-547, 1978.
Yakov Amihud.
The effect of uncertainty in input quantities on the optimal
expected input combination.
Manage. Sci. 23, 957-962, 1977.
H. M. Amman and D. A. Kendrick.
Stochastic policy design in a learning environment with rational
expectations.
J. Optim. Theory Appl., 105(3):509-520, 2000.
Special Issue in honor of Professor David G. Luenberger.
Hans M. Amman, David A. Kendrick, and Sudhakar Achath.
Solving stochastic optimization models with learning and rational
expectations.
Econom. Lett., 48(1):9-13, 1995.
G. Anandalingam.
A stochastic programming process model for investment planning.
Comput. Oper. Res. 14, 521-536, 1987.
Yu. G. Anastasyan, V.I. Gershovich, B.A. Yaroshevich, È. I. Nenakhov, O.T.
Burlak, M.B. Shchepakin, and G.G. Murauskas.
O nekotorykh algoritmakh negladkoi optimizatsii i
diskretnogo programmirovaniya, volume 6 of Preprint 81.
Akad. Nauk Ukrain. SSR Inst. Kibernet., Kiev, 1981.
E. J. Anderson and A. B. Philpott.
On supply function bidding in electricity markets.
In C. Greengard and A. Ruszczy\'nski, editors, Decision Making
under Uncertainty: Energy and Power, volume 128 of IMA volumes on
Mathematics and its Applications, pages 115-134. Springer-Verlag, 2002.
S. Andradóttir.
A method for discrete stochastic optimization.
Management Science 1946-1961, 1995.
S. Andradóttir.
A stochastic approximation algorithm with varying bounds.
Operations Research 1037-1048, 1995.
S. Andradóttir.
A global search method for discrete stochastic optimization.
SIAM Journal on Optimization 513-530, 1996.
S. Andradóttir.
Optimization of the transient and steady-state behavior of discrete
event systems.
Management Science 717-737, 1996.
S. Andradóttir.
A scaled stochastic approximation algorithm.
Management Science 475-498, 1996.
S. Andradóttir.
Simulation optimization.
Handbook on Simulation (edited by Jerry Banks), Chapter
9, John Wiley and Sons New York, to appear.
Mikhail Andramonov, Jerzy Filar, Panos Pardalos, and Alexander Rubinov.
Hamiltonian cycle problem via Markov chains and min-type
approaches.
In Approximation and complexity in numerical optimization
(Gainesville, FL, 1999), pages 31-47. Kluwer Acad. Publ., Dordrecht, 2000.
G. Andreatta.
Shortest path models in stochastic networks.
In G. Andreatta, F. Mason, and P. Serafini, editors, Stochastics
in Combinatorial Optimization, pages 178-186, Singapore, 1987. CISM,
Udine, World Scientific Publishing Co. Pte. Ltd.
G. Andreatta and F. Mason.
k-Eccentricity and absolute k-centrum of a probabilistic tree.
Eur. J. Oper. Res. 19, 114-117, 1985.
G. Andreatta and G. Romanin-Jacur.
Aircraft flow management under congestion.
Transportation Science, 21(4):249-253, 1987.
G.B. Andreatta, G. Salinetti, and R.J.-B. Wets, editors.
Stochastic programming.
Baltzer Science Publishers BV, Amsterdam, 1995.
Papers from the Sixth International Conference held in Udine,
September 14-18, 1992, Ann. Oper. Res. 56 (1995).
Giovanni Andreatta and Luciano Romeo.
Stochastic shortest paths with recourse.
Networks 18, No.3, 193-204, 1988.
Giovanni Andreatta and Wolfgang J. Runggaldier.
An approximation scheme for stochastic dynamic optimization problems.
Math. Programming Stud., 27:118-132, 1986.
Stochastic programming 84. I.
Colette Andrieu.
Sur les solutions fiables d'un problème stochastique d'optimisation
sous contrainte.
Rev. Roumaine Math. Pures Appl., 25(5):677-694, 1980.
Colette Andrieu.
Sur certaines solutions fiables d'un problème stochastique de
recherche optimale.
Math. Operationsforsch. Statist. Ser. Optim., 12(1):115-122,
1981.
Y.P. Aneja and K.P.K. Nair.
Maximal expected flow in a network subject to arc failures.
Networks 10, 45-57, 1980.
R. Anghelescu and V. Anghelescu.
Recherches sur la programmation lineaire stochastique a recours.
(Research on linear stochastic programming with recourse).
In Proc. Symp. Math. Appl., Timisoara/Rom. 1985, 163-165,
1986.
R. Anghelescu and V. Anghelescu.
étude sur la programmation linéaire stochastique.
In Proceedings of the Second Symposium of Mathematics and its
Applications (Timi soara, 1987), pages 217-220, Timi soara, 1988. Res.
Centre Acad. SR Romania.
R. Anghelescu and V. Anghelescu.
Modalités d'approche des problèmes stochastiques d'optimum
vectoriel. I.
In Proceedings of the Third Symposium of Mathematics and its
Applications (Timi soara, 1989), pages 123-128, Timi soara, 1990. Rom.
Acad.
R. Anghelescu and V. Anghelescu.
Solution optimale dans la programmation stochastique vectorielle.
I.
In Proceedings of the Fourth Symposium of Mathematics and its
Applications (Timi soara, 1991), pages 16-21, Timi soara, 1992. Mirton.
Rodica Anghelescu and V. Anghelescu.
Methods in determinating the optimal solution in problems of linear
stochastic programming.
Bul. Stiin t. Tehn. Univ. Tehn. Timi soara Mat. Fiz.,
38(52):94-101, 1993.
Z.P. Anisimova and O.R. Petrenko.
Limit theorems of stochastic optimization procedures in Markov
random environments.
In Stochastic systems and their applications (Russian), pages
4-8. Akad. Nauk Ukrain. SSR Inst. Mat., Kiev, 1990.
A.N. Antamoshkin and M.A. Valishevskij.
Complete effectivity investigation of methods optimizing functionals
with Boolean derivatives taking random search as example.
In Applications of random search to the solution of applied
problems, Collect. sci. Works, Kemerovo 1982, 48-54, 1982.
K.A. Antanavichyus and S.S. Chirba.
A stochastic programming problem for preparing rational production
programs for branch complexes.
Ehkon. Mat. Metody 21, 1048-1057, 1985.
K.A. Antanavichyus and S.S. Chirba.
A problem of stochastic programming for preparation of rational
production programs for branch complexes.
Èkonom. i Mat. Metody, 21(6):1048-1057, 1985.
P.L. Antonelli and J.M. Skowronski.
Adaptive identification of environmental stress for the management
of plant growth.
Math. Comput. Modelling 10, No.1, 27-35, 1988.
G.E. Antonov and V. Ja. Katkovnik.
A generalization of the concept of statistical gradient.
Avtomat. i Vycisl. Tehn. (Riga), 4:25-30, 1972.
I.L. Antonov.
A steady-state process in a two-dimensional extremal system in the
presence of prohibited regions and a random method of search.
Vestnik Moskov. Univ. Ser. I Mat. Meh., 25(5):117-122, 1970.
I.L. Antonov.
A transition process in a two-dimensional extremal system in the
presence of forbidden domains, in a random method of search.
In Problems of statistical optimization (Russian), pages
69-80. Izdat. "Zinatne", Riga, 1971.
Bruno Apolloni and Ferdinando Pezzella.
Confidence intervals in the solution of stochastic integer linear
programming problems.
In Stochastics and optimization, Sel. Pap. ISSO Meet.,
Gorguano/Italy 1982, Ann. Oper. Res. 1, 67-78, 1984.
N.I. Arbuzova.
Ueber die stochastische .-Stabilitaet der Loesung einer Aufgabe
der quadratischen Programmierung.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1967, Nr. 3, 35-40, 1967.
N.I. Arbuzova.
Interdependence of the stochastic e-stabilities of linear
and linear fractional programming problems of a special form.
Èkonom. i Mat. Metody, 4:108-110, 1968.
N.I. Arbuzova and V.L. Danilov.
Zur Erweiterung des Begriffs der Stabilitaet des Problems der
linearen Programmierung.
Kibernetika, Kiev 1970, No.4, 139-140, 1970.
F. Archetti.
Evaluation of random gradient techniques for unconstrained
optimization.
Calcolo 12, 83-94, 1975.
F. Archetti, G. Di Pillo, and M. Lucertini, editors.
Stochastic programming, volume 76 of Lecture Notes in
Control and Information Sciences, Berlin, 1986. Springer-Verlag.
Papers from the conference held in Gargnano, September 15-21, 1983.
F. Archetti and F. Frontini.
The application of a global optimization method to some
technological problems.
In Towards global optimisation 2, Work shops Varenna 1976 and
Bergamo 1977, 179-188, 1978.
F. Archetti, A. Gaivoronski, and A. Sciomachen.
Sensitivity analysis and optimization of stochastic Petri nets.
Discrete Event Dyn. Syst. 3, No.1, 5-37, 1993.
F. Archetti and F. Schoen.
A survey on the global optimization problem: General theory and
computational approaches.
In Stochastics and optimization, Sel. Pap. ISSO Meet.,
Gorguano/Italy 1982, Ann. Oper. Res. 1, 87-110, 1984.
Francesco Archetti.
Analysis of stochastic strategies for the numerical solution of the
global optimization problem.
In Numerical techniques for stochastic systems, Conf.,
Gargnano/Italy 1979, 275-295, 1980.
Francesco Archetti.
A probabilistic algorithm for global optimization problems with a
dimensionality reduction technique.
In Optimization techniques (Proc. Ninth IFIP Conf., Warsaw,
1979), Part 2, volume 23 of Lecture Notes in Control and Information
Sci., pages 36-42, Berlin, 1980. Springer.
Francesco Archetti and Bruno Betrò.
Stochastic models and optimization.
Boll. Un. Mat. Ital. A (5), 17(2):295-301, 1980.
T. W. Archibald, C. S. Buchanan, K. I. M. McKinnon, and L. C. Thomas.
Nested Benders decomposition and dynamic programming for reservoir
optimisation.
Journal of the Operational Research Society, 50(5):468-479,
1999.
T. W. Archibald, C. S. Buchanan, L. C. Thomas, and K. I. M. McKinnon.
Controlling multi-reservoir systems.
European Journal of Operational Research, 129(3):619-626,
2001.
R. Ardanuy and A. Alcalá.
Weak infinitesimal operators and stochastic differential games.
Stochastica, 13(1):5-12, 1992.
K. A. Ariyawansa, C. Cacho, and A. J. Felt.
A family of stochastic programming test problems based on a model for
tactical manpower planning.
J. Math. Model. Algorithms, 4(4):369-390, 2005.
K. A. Ariyawansa and Andrew J. Felt.
On a new collection of stochastic linear programming test problems.
INFORMS J. Comput., 16(3):291-299, 2004.
K. A. Ariyawansa and Yuntao Zhu.
Stochastic semidefinite programming: a new paradigm for stochastic
optimization.
4OR, 4(3):239-253, 2006.
K. A. Ariyawansa and Yuntao Zhu.
A class of volumetric barrier decomposition algorithms for stochastic
quadratic programming.
Appl. Math. Comput., 186(2):1683-1693, 2007.
K.A. Ariyawansa.
Performance of a benchmark implementation of the Van Slyke and Wets
algorithm for stochastic programs on the alliant FX/8.
In Dongarra, Jack (ed.) et al., Proceedings of the fifth SIAM
conference on parallel processing for scientific computing, held in Houston,
TX, USA, March 25-27, 1991. Philadelphia, PA: SIAM, (ISBN 0-89871-303-X/pbk).
186- 192, 1992.
Vadim I. Arkin.
Stochastic optimization approach to dynamic problems with jump
changing structure.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 104-110. Springer, Berlin, 1998.
V.I. Arkin.
Economic dynamics and discretely varying technology. Probabilistic
approach.
In Probability and mathematical economics, Moskva, 3-30,
1988.
V.I. Arkin and I.V. Evstigneev.
Probabilistic models of control and economic dynamics.
(Veroyatnostnye modeli upravleniya i ehkonomicheskoj dinamiki).
Moskva: "Nauka"., 1979.
V.I. Arkin, A. Shiraev, and R. Wets, editors.
Stochastic Optimization. Proceedings of the International
Conference, Kiev 1984.
Springer, Berlin, 1986.
LN in Control and Information Sciences 81.
V.I. Arkin and S.A. Smolyak.
On the structure of optimality criteria in stochastic optimization
models.
In Stochastic optimization, Proc. Int. Conf., Kiev/USSR 1984,
Lect. Notes Control Inf. Sci. 81, 275-286, 1986.
Ronald D. Armstrong and Joseph L. Balintfy.
A chance constrained multiple choice programming algorithm with
applications.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 301-325. Academic Press, London, 1980.
Klaus-Peter Arnold.
Stochastische Transportprobleme.
Verlag Dr. Kovac, Hamburg, 1987.
H. Arsham.
Performance extrapolation in discrete-event systems simulation.
International Journal of Systems Science, 27(9):863-869, 1996.
H. Arsham.
Stochastic optimization of discrete event systems simulation.
International Journal of Microelectronics and Reliability,
36(10):1357-1368, 1996.
H. Arsham.
Goal seeking problem in discrete event systems simulation.
International Journal of Microelectronics and Reliability,
37(3):391-395, 1997.
H. Arsham.
Algorithms for sensitivity information in discrete-event systems
simulation.
Simulation Practice and Theory, 6(1):1-22, 1998.
H. Arsham.
Techniques for Monte Carlo optimizing.
Monte Carlo Methods Appl., 4(3):181-229, 1998.
Zvi Artstein.
Convergence rates for the optimal values of allocation processes.
Math. Oper. Res. 9, 348-355, 1984.
Zvi Artstein.
Limit laws for multifunctions applied to an optimization problem.
In Multifunctions and integrands (Catania, 1983), volume 1091
of Lecture Notes in Math., pages 66-79. Springer, Berlin, 1984.
Zvi Artstein.
Probing for information in two-stage stochastic programming and the
associated consistency.
In Asymptotic statistics (Prague, 1993), Contrib. Statist.,
pages 21-33. Physica, Heidelberg, 1994.
Zvi Artstein.
Sensitivity with respect to the underlying information in stochastic
programs.
J. Comput. Appl. Math., 56(1-2):127-136, 1994.
Stochastic programming: stability, numerical methods and applications
(Gosen, 1992).
Zvi Artstein.
Gains and costs of information in stochastic programming.
Ann. Oper. Res., 85:129-152, 1999.
Stochastic programming. State of the art, 1998 (Vancouver, BC).
Zvi Artstein and Roger J.-B. Wets.
Approximating the integral of a multifunction.
J. Multivariate Anal., 24(2):285-308, 1988.
Zvi Artstein and Roger J.-B. Wets.
Sensors and information in optimization under stochastic uncertainty.
Math. Oper. Res., 18(3):523-547, 1993.
Zvi Artstein and Roger J.-B. Wets.
Stability results for stochastic programs and sensors, allowing for
discontinuous objective functions.
SIAM J. Optim., 4(3):537-550, 1994.
Zvi Artstein and Roger J.-B. Wets.
Consistency of minimizers and the SLLN for stochastic
programs.
J. Convex Anal., 2(1-2):1-17, 1995.
Enrique R. Arzac.
Profits and safety in the theory of the firm under price
uncertainty.
Internat. econom. Review 17, 163-171, 1976.
R.W. Ashford and E.M.L. Beale.
A cross-estimation technique for using control variables in
stochastic simulations.
European J. Oper. Res., 40(3):352-362, 1989.
Soeren Asmussen and Reuven Rubinstein.
Performance evaluation for the score function method in sensitivity
analysis and stochastic optimization.
In Simulation and optimization, Proc. Int. Workshop Comput.
Intensive Methods Simulation Optimization, Laxenburg/Austria 1990, Lect.
Notes Econ. Math. Syst. 374, 1-13, 1992.
P.G. Asojanc.
A problem of Robbins.
Kibernetika (Kiev), 2:105-106, 1976.
A. S. Asratyan and N. N. Kuzyurin.
Approximation of optima of integer programs of covering-packing type.
Diskret. Mat., 12(1):96-106, 2000.
N.V. Astanovskaya, V.I. Varshavskij, V.P. Grigorenko, and V.M. Revako.
Adaptive algorithms for the decentralized distribution of resources
under conditions of uncertainty.
Probl. Sluchajnogo Poiska 7, 296-303, 1978.
H. Attouch.
Variational averaging of problems with stochastic data.
In Inverse methods in action, Proc. Multicent. Meet.,
Montpellier/Fr. 1989, 396-404, 1990.
H. Attouch and R.J-B. Wets.
Quantitative stability of variational systems i: the epigraphical
distance.
Transactions of the American Mathematical Society,
328:695-729, 1991.
Kelly T. Au, Julia L. Higle, and Suvrajeet Sen.
Inexact subgradient methods with applications in stochastic
programming.
Math. Programming, 63(1, Ser. A):65-82, 1994.
I.L. Averbakh.
An algorithm for solving an m-dimensional knapsack problem with
random coefficients.
Diskret. Mat., 2(3):3-9, 1990.
I.L. Averbakh.
An additive method for optimization of two-stage stochastic systems
with discrete variables.
Sov. J. Comput. Syst. Sci. 28, No.4, 161-165 translation from
Izv. Akad. Nauk SSSR, Tekh. Kibern. 1990, No.1, 162-166 (1990)., 1990.
I.L. Averbakh.
An iterative decomposition method in one-stage problems of stochastic
integer programming.
Zh. Vychisl. Mat. i Mat. Fiz., 30(10):1467-1476, 1990.
I.L. Averbakh.
An iterative method of solving two-stage discrete stochastic
programming problems with additively separable variables.
Comput. Math. Math. Phys. 31, No.6, 21-27 translation from Zh.
Vychisl. Mat. Mat. Fiz. 31, No.6, 810-818 (1991)., 1991.
David Avis.
On the extreme rays of the metric cone.
Canad. J. Math., 32(1):126-144, 1980.
M. Avriel and D.J. Wilde.
Stochastic geometric programming.
In Proceedings of the Princeton Symposium on Mathematical
Programming (Princeton Univ., 1967), pages 73-91, Princeton, N.J., 1970.
Princeton Univ. Press.
M. Avriel and A.C. Williams.
The value of information and stochastic programming.
Oper. Res. 18, 947-954, 1970.
S.V. Avrutik and N.P. Dekanova.
Two approaches in nonlinear and stochastic programming.
In Numerical methods in analysis (applied mathematics)
(Russian), pages 5-11. Akad. Nauk SSSR Sibirsk. Otdel. Sibirsk. Ènerget.
Inst., Irkutsk, 1976.
S.V. Avrutin.
On the formulation and solution of some non-linear stochastic
problems.
In Optimization methods and their applications, 9, Work
Collect., Irkutsk 1979, 5-13, 1979.
Shimon Awerbuch, Joseph G. Ecker, and William A. Wallace.
A note: Hidden nonlinearities in the application of goal
programming.
Management Sci. 22, 918-920, 1976.
Abbas N. Azad and Naveed Saleem.
A chance constrained approach to bank balance sheet management: A
simple model with a computational example.
J. Inf. Optimization Sci. 12, No.3, 363-386, 1991.
F. Azadivar and J. Talavage.
Optimization of stochastic simulation models.
Math. Comput. Simulation 22, 231-241, 1980.
Farhad Azadivar and Young-Hae Lee.
Optimization of discrete variable stochastic systems by computer
simulation.
Math. Comput. Simulation 30, No.4, 331-345, 1988.
Yossi Azar and Yossi Richter.
An improved algorithm for CIOQ switches.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
N. Baba.
Global optimization of functions by the random optimization method.
Int. J. Control 30, 1061-1065, 1979.
N. Baba.
Convergence of a random optimization method for constrained
optimization problems.
J. Optimization Theory Appl. 33, 451-461, 1981.
N. Baba.
A hybrid algorithm for finding a global minimum.
Int. J. Control 37, 929-942, 1983.
Norio Baba and Akira Morimoto.
Three approaches for solving the stochastic multiobjective
programming problem.
In Stochastic optimization. Numerical methods and technical
applications, Proc. GAMM/IFIP-Workshop, Neubiberg/Ger. 1990, Lect. Notes
Econ. Math. Syst. 379, 93-109, 1992.
Norio Baba and Akira Morimoto.
Stochastic approximation method for solving the stochastic
multiobjective programming problem.
Internat. J. Systems Sci., 24(4):789-796, 1993.
A.S. Babanin.
A generalization of the arctangent law.
Teor. Veroyatnost. i Mat. Statist., 28:3-5, 1983.
Léonard Bacaud, Claude Lemaréchal, Arnaud Renaud, and Claudia
Sagastizábal.
Bundle methods in stochastic optimal power management: a
disaggregated approach using preconditioners.
Comput. Optim. Appl., 20(3):227-244, 2001.
Thomas Bäck.
Evolutionary algorithms in theory and practice.
The Clarendon Press Oxford University Press, New York, 1996.
Evolution strategies, evolutionary programming, genetic algorithms.
Anna Badach.
An application of reliability theory to stochastic linear programming
problems.
Przeglk ad Statyst., 22(4):569-584, 1975.
A.M. Bagdasarov and G.A. Samatov.
Ein Modell der Perspektivplanung der Arbeit eines Transportsystems.
Dokl. Akad. Nauk UzSSR 1978, No.11, 15-17, 1978.
Adib Bagh and Michael Casey.
An ergodic theorem for random lagrangians with an application to
stochastic programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Adib Bagh and Michael S. Casey.
An ergodic theorem for random Lagrangians with an application to
stochastic programming.
Int. J. Pure Appl. Math., 14(3):343-363, 2004.
O. Bahn, O. Du Merle, J.-L. Goffin, and J.-P. Vial.
A cutting plane method from analytic centers for stochastic
programming.
Math. Programming, 69(1, Ser. B):45-73, 1995.
Nondifferentiable and large-scale optimization (Geneva, 1992).
D. Bai, T.J. Carpenter, and J.M. Mulvey.
Making a case for robust models.
Management Science, 43:895-907, 1997.
Matthew D. Bailey, Steven M. Shechter, and Andrew J. Schaefer.
SPAR: stochastic programming with adversarial recourse.
Oper. Res. Lett., 34(3):307-315, 2006.
Michael P. Bailey.
Solving a class of stochastic minimization problems.
Oper. Res. 42, No.3, 428-438, 1994.
T.G. Bailey, P. Jensen, and D.P. Morton.
Response surface analysis of two-stage stochastic linear programming
with recourse.
Naval Research Logistics, 46:753-778, 1999.
M.M. Bajzel' and G.S. Tarasenko.
The investigation of an adaptive optimization algorithm in the
situation of noise.
Probl. Sluchajnogo Poiska 9, 106-124, 1981.
V. Balachandran.
Generalized transportation networks with stochastic demands: an
operator-theoretic approach.
Networks, 9(2):169-184, 1979.
A.V. Balakrishnan.
Stochastic optimization theory in Hilbert spaces. I.
Appl. Math. Optimization 1, 97-120, 1974.
Egon Balas.
Nonconvex quadratic programming via generalized polars.
SIAM J. Appl. Math., 28:335-349, 1975.
E.J. Balder.
An extension of the usual model in statistical decision theory with
applications to stochastic optimization problems.
J. Multivariate Anal., 10(3):385-397, 1980.
Erik J. Balder.
Existence without explicit compactness in stochastic dynamic
programming.
Math. Oper. Res., 17(3):572-580, 1992.
J.F. Balducchi, G. Cohen, J.-C. Dodu, M. Goursat, Herz, J.-P. Quadrat, and
M. Viot.
Three methods for optimizing the capacities of an electrical
transmission network.
8th IFAC World Congress, Kyoto, Japan, 1981.
J.L. Balintfy.
Nonlinear programming for models with joint chance constraints.
In Integer nonlin. Program., 337-352, 1970.
Joseph L. Balintfy.
Applications of mathematical programming to optimize human diets.
In Symp. Math. 19, Program. mat. Appl., Convegno 1974,
313-339, 1976.
Michael O. Ball, Robert Hoffman, Amedeo R. Odoni, and Ryan Rifkin.
A stochastic integer program with dual network structure and its
application to the ground-holding problem.
Oper. Res., 51(1):167-171, 2003.
Enrique Ballestero.
Stochastic goal programming: a mean-variance approach.
European J. Oper. Res., 131(3):476-481, 2001.
Enrique Ballestero.
Stochastic linear programming with scarce information: an approach
from expected utility and bounded rationality applied to the textile
industry.
Eng. Optim., 38(4):425-440, 2006.
Simonetta Balsamo, Vittoria de Nitto Personé, and Raif Onvural.
Analysis of queueing networks with blocking.
Kluwer Academic Publishers, Boston, MA, 2001.
Shumeet Baluja.
Using a priori knowledge to create probabilistic models for
optimization.
Internat. J. Approx. Reason., 31(3):193-220, 2002.
Synergies between evolutionary computation and probabilistic
graphical models.
A.K. Balyko.
A problem of stochastic programming.
Engrg. Cybernetics, 20(1):43-49 (1983), 1982.
Julio R. Banga and Warren D. Seider.
Global optimization of chemical processes using stochastic
algorithms.
In State of the art in global optimization (Princeton, NJ,
1995), volume 7 of Nonconvex Optim. Appl., pages 563-583. Kluwer
Acad. Publ., Dordrecht, 1996.
Peter Bank and Frank Riedel.
Optimal consumption choice with intertemporal substitution.
Ann. Appl. Probab., 11(3):750-788, 2001.
G. Bao and C.G. Cassandras.
Stochastic comparison algorithm for continuous optimization with
estimation.
J. Optim. Theory Appl., 91(3):585-615, 1996.
A.P. Baranov.
Convergence of a class of stochastic approximation procedures.
Kibernetika (Kiev), 4:64-69, 135, 1987.
V.V. Baranov.
A dynamic programming algorithm in stochastic systems.
U.S.S.R. Comput. Math. Math. Phys. 18, No.6, 55-68 translation
from Zurn. vycislit. Mat. mat. Fiz. 18, No.6, 1416-1429 (1978)., 1979.
V.V. Baranov.
Optimal dynamic pattern recognition and decision making.
Zh. Vychisl. Mat. i Mat. Fiz., 34(7):984-1000, 1994.
V.V. Baranov and N.V. Tret'yakova.
The averaging sequence method in Markov decision processes with
periodic strategies.
Vestnik Khar' kov. Gos. Univ., 286:34-50, ii, 1986.
J. F. Bard, D. P. Morton, and Y. Wang.
Workforce planning at USPS mail processing distribution centers
using stochastic optimization.
Annals of Operations Research, 155:51-78, 2007.
Jacob Barhen, Vladimir Protopopescu, and David Reister.
TRUST: a deterministic algorithm for global optimization.
Science, 276(5315):1094-1097, 1997.
Bill Baritompa and Eligius M. T. Hendrix.
On the investigation of stochastic global optimization algorithms.
J. Global Optim., 31(4):567-578, 2005.
W. P. Baritompa, M. Dür, E. M. T. Hendrix, L. Noakes, W. J. Pullan, and
G. R. Wood.
Matching stochastic algorithms to objective function landscapes.
J. Global Optim., 31(4):579-598, 2005.
W.P. Baritompa, Zhang Baoping, R.H. Mladineo, G.R. Wood, and Z.B. Zabinsky.
Towards pure adaptive search.
J. Global Optim., 7(1):93-110, 1995.
David P. Baron.
Information in two-stage programming under uncertainty.
Naval Res. Logist. Quart., 18:169-176, 1971.
Diana Barro and Elio Canestrelli.
A decomposition approach in multistage stochastic programming.
Rend. Studi Econ. Quant., pages 73-88, 2005.
F.Hutton Barron and Kenneth D. Mackenzie.
A constrained optimization model of risky decisions.
J. math. Psychology 10, 60-72, 1973.
Russell R. Barton.
Minimization algorithms for functions with random noise.
Amer. J. Math. Management Sci., 4(1-2):109-138, 1984.
Statistics and optimization : the interface.
A.M. Bartsalkin and D.K. Zambitskij.
On an allocation problem with probabilistic demands.
Mat. Issled. 87, 10-15, 1986.
Kengy Barty, P. Carpentier, J.-P. Chancelier, G. Cohen, M. de Lara, and
T. Guilbaud.
Dual effect free stochastic controls.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Kengy Barty, Jean-Sebastien Roy, and Cyrille Strugarek.
A perturbed gradient algorithm in hilbert spaces.
Optimization Online, http://www.optimization-online.org, 2005.
Kengy Barty, Jean-Sebastien Roy, and Cyrille Strugarek.
A stochastic gradient type algorithm for closed loop problems.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
A.I. Barvinok.
Statistical sums in optimization and computational problems.
Algebra i Analiz, 4(1):3-53, 1992.
Alexander Barvinok.
Approximate counting via random optimization.
Random Structures Algorithms, 11(2):187-198, 1997.
A.M. Barzalkin and D.K. Zambitskij.
An algorithm for the solution of a stochastic allocation problem.
Mat. Issled. 96, 3-7, 1987.
Tamer Ba sar.
A general theory for Stackelberg games with partial state
information.
Large Scale Systems, 3(1):47-56, 1982.
John Basel, III and Thomas R. Willemain.
Random tours in the traveling salesman problem: analysis and
application.
Comput. Optim. Appl., 20(2):211-217, 2001.
Cock Bastian and Alexander H.G. Rinnooy Kan.
The stochastic vehicle routing problem revisited.
Eur. J. Oper. Res. 56, No.3, 407-412, 1992.
Fabian Bastin.
An adaptive trust-region approach for nonlinear stochastic
optimisation with an application in discrete choice theory.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Fabian Bastin, Cinzia Cirillo, and Philippe L. Toint.
Convergence theory for nonconvex stochastic programming with an
application to mixed logit.
Math. Program., 108(2-3, Ser. B):207-234, 2006.
Vic Baston and Anatole Beck.
Generalizations in the linear search problem.
Israel J. Math., 90(1-3):301-323, 1995.
Dipak R. Basu and Alexis Lazaridis.
Evaluation of intersectoral investment allocation in India,
1951-1970. An application of stochastic optimal control by pseudo-inverse.
Int. J. Syst. Sci. 11, 889-906, 1980.
Vladimir Batagelj.
Notes on the dynamic clusters method.
In IV conference on applied mathematics (Split, 1984), pages
139-146. Univ. Split, Split, 1985.
John Bather.
Randomised allocation of treatments in sequential trials.
Adv. Appl. Probab. 12, 174-181, 1980.
V.D. Batukhtin and L.N. Korotaeva.
A stochastic method for searching of an extremum of discontinuous
functions.
In Nonsmooth problems of optimization and control (Russian),
pages 4-11, 111, Sverdlovsk, 1988. Akad. Nauk SSSR Ural. Otdel.
V.D. Batukhtin and L.A. Maiboroda.
Optimization of Discontinuous Functions.
Nauka, Moscow, 1984.
(in Russian).
V.D. Batukhtin and L.A. Majboroda.
On a formalization of extremal problems.
Sov. Math., Dokl. 21, 1-5 translation from Dokl. Akad. Nauk SSSR
250, 11-14 (1980)., 1980.
Vijay S. Bawa.
On chance constrained programming problems with joint constraints.
Management Sci., 19:1326-1331, 1972/73.
V.S. Bawa.
A simple concavity condition for a class of chance-constrained
programming problems with joint constraints.
Operations Res. 24, 378-380, 1976.
David S. Bayard.
Proof of quasi-adaptivity for the m-measurement feedback class of
stochastic control policies.
IEEE Trans. Autom. Control AC-32, 447-451, 1987.
Güzin Bayraksan and David P. Morton.
Assessing solution quality in stochastic programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Güzin Bayraksan and David P. Morton.
Assessing solution quality in stochastic programs.
Math. Program., 108(2-3, Ser. B):495-514, 2006.
M.S. Bazaraa, J.J. Goode, and M.Z. Nashed.
On the cones of tangents with applications to mathematical
programming.
J. Optimization Theory Appl., 13:389-426, 1974.
L.G. Bazenov and A.M. Gupal.
A certain stochastic analogue of the method of feasible directions.
Kibernetika (Kiev), 5:94-95, 1973.
Z.A. Bazhenova.
Choice models for rational strategies to develop specialized and
industrial capacities of the industrial base in case of indetermination.
Issled. Oper. ASU 20, 36-44, 1982.
E.M.L. Beale.
Some uses of mathemtical programming systems to solve problems that
are not linear.
Operational Res. Quart., 26(3, part 2):609-618, 1975.
E.M.L. Beale, G.B. Dantzig, and R.D. Watson.
A first order approach to a class of multi-time-period stochastic
programming problems.
Math. Programming Stud., 27:103-117, 1986.
Stochastic programming 84. I.
Luca Becchetti, Stefano Leonardi, Alberto Marchetti-Spaccamela, Guidouca
Schaefer, and Tjark Vredeveld.
Average Case and Smoothed Competitive Analysis of the Multi-Level
Feedback Algorithm.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
A.A. Bedel'baev, A.B. Kopeikin, and Ju.S. Popkov.
Efficiency of mixed strategies in optimization problems.
Autom. Remote Control 3 1372-1377 (1977), translation from
Avtom. Telemekh. 1976, No.9, 90-95 (1976)., 1976.
C.N. Beer and B.L. Foote.
A procedure to rank bases by probability of being optimal using
imbedded hyperspheres.
Math. Programming, 9(1):123-128, 1975.
Zuzana Beerliova, Felix Eberhard, Thomas Erlebach, Alexander Hall, Michael
Hoffmann, Matus Mihalak, and L. Shankar Ram.
Network Discovery and Verification.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
H. Beigy and M. R. Meybodi.
Stochastic optimization using continuous action-set learning
automata.
Sci. Iran., 12(1):14-25, 2005.
¯I. V. Beiko and P.N. Zin'ko.
Some probability estimates for algorithms of stochastic programming
with a constant step multiplier.
Kibernetika (Kiev), 2:91-95, 1978.
V.Z.(ed.) Belen'kii and V.A.(ed.) Volkonskii.
Iterative Methoden in der Theorie der Spiele und
Programmierung. (Iterativnye metody v teorii igr i programmirovanii.).
Ekonomiko-matematiceskaja biblioteka. Moskau: Verlag 'Nauka',
Hauptredaktion fuer physikalisch-mathematische Literatur., 1974.
Claude Bélisle, Arnon Boneh, and Richard J. Caron.
Convergence properties of hit-and-run samplers.
Comm. Statist. Stochastic Models, 14(4):767-800, 1998.
R. Bellman and M. Roosta.
A stochastic travelling salesman problem.
Stochastic Anal. Appl. 1, 159-161, 1983.
Ju. A. B¯elov.
A two-level stochastic programming problem.
Dokl. Akad. Nauk Ukrain. SSR Ser. A, 5:371-375, 400, 1979.
Ju. A. B¯elov.
Block stochastic optimization problem with probabilistic constraints
and deterministic solution.
Dokl. Akad. Nauk Ukrain. SSR Ser. A, 8:63-66, 89, 1980.
Ju. A. B¯elov.
Monotonicity of a functional for an iterative algorithm for the block
problem with random parameters.
Zh. Vychisl. Mat. i Mat. Fiz., 20(2):298-305, 549, 1980.
Ju. A. B¯elov.
Two-level optimization for a single-step stochastic programming
problem with probability constraints.
Vychisl. Prikl. Mat. (Kiev), 43:78-85, 158, 1981.
Yu. A. B¯elov.
The block two-stage stochastic programming problem with continuous
distribution of the constraint vector.
Cybernetics, 16(1):145-148, 1980.
Yu. A. B¯elov.
Decomposition of two-stage problems of stochastic programming of
block type.
Dokl. Akad. Nauk SSSR, 255(4):811-813, 1980.
Yu. A. Belov.
A stochastic normal model with solving distributions for block-type
extremal problems.
Cybernetics, 19(2):239-247, 1983.
Yu.A. Belov.
Two-level problem of stochastic programming.
Dopov. Akad. Nauk Ukr. RSR, Ser. A 1979, 370-374, 1979.
Yu.A. Belov.
Monotonicity with respect to functional of an iterative algorithm
for the block problem with random parameters.
U.S.S.R. Comput. Math. Math. Phys. 20, No.2, 24-32 translation
from Zh. Vychisl. Mat. Mat. Fiz. 20, 298-305 (1980)., 1980.
Yu.A. Belov.
A single-stage block type stochastic problem with row probability
constraints.
U.S.S.R. Comput. Math. Math. Phys. 21, No.5, 58-64 translation
from Zh. Vychisl. Mat. Mat. Fiz. 21, 1133-1139 (1981)., 1981.
Yu.A. Belov, A.I. Yastremskij, and D.V. Usovskij.
Experimental design in the imitative modelling with the help of
methods of stochastic programming.
Vychisl. Prikl. Mat., Kiev 48, 133-139, 1982.
L.S. Belyaev.
Some statements and ways of solving dynamic optimization problems
under uncertainty.
In Optim. Techn., IFIP techn. Conf. Novosibirsk 1974, Lect.
Notes Comput. Sci. 27, 18-21, 1975.
L.S. Belyaev.
The solution of complex optimization problems under uncertainty
conditions. (Reshenie slozhnykh optimizatsionnykh zadach v usloviyakh
neopredelennosti).
Akademiya Nauk SSSR, Sibirskoe Otdelenie, Sibirskij Ehnergeticheskij
Institut. Novosibirsk: Izdatel'stvo "Nauka", Sibirskoe Otdelenie., 1978.
F. Ben Abdelaziz, P. Lang, and R. Nadeau.
Pointwise efficiency in multiobjective stochastic linear
programming.
J. Oper. Res. Soc. 45, No.11, 1324-1334, 1994.
F. Ben Abdelaziz, P. Lang, and R. Nadeau.
Distributional unanimity in multiobjective stochastic linear
programming.
In Multicriteria analysis (Coimbra, 1994), pages 225-236.
Springer, Berlin, 1997.
F. Ben Abdelaziz, P. Lang, and R. Nadeau.
Dominance and efficiency in multiobjective stochastic linear
programming.
In Advances in multiple objective and goal programming
(Torremolinos, 1996), volume 455 of Lecture Notes in Econom. and Math.
Systems, pages 160-169. Springer, Berlin, 1997.
F. Ben Abdelaziz and H. Masri.
Stochastic programming with fuzzy linear partial information on
probability distribution.
European J. Oper. Res., 162(3):619-629, 2005.
Yakov Ben-Haim and Isaac Elishakoff.
Convex models of uncertainty in applied mechanics.
Studies in Applied Mechanics 25. Elsevier, Amsterdam, 1990.
Adi Ben-Israel and Aharon Ben-Tal.
Duality and equilibrium prices in economics of uncertainty.
Math. Methods Oper. Res., 46(1):51-85, 1997.
A. Ben-Tal and E. Hochman.
More bounds on the expectation of a convex function of a random
variable.
J. Appl. Probability, 9:803-812, 1972.
A. Ben-Tal and M. Teboulle.
Penalty functions and duality in stochastic programming via
f-divergence functionals.
Math. Oper. Res., 12(2):224-240, 1987.
Aharon Ben-Tal.
The entropic penalty approach to stochastic programming.
Math. Oper. Res., 10(2):263-279, 1985.
Aharon Ben-Tal and Adi Ben-Israel.
A recourse certainty equivalent for decisions under uncertainty.
Ann. Oper. Res., 30(1-4):3-44, 1991.
Stochastic programming, Part I (Ann Arbor, MI, 1989).
Aharon Ben-Tal and Eithan Hochman.
Stochastic programs with incomplete information.
Operations Res., 24(2):336-347, 1976.
Aharon Ben-Tal, Tamar Margalit, and Arkadi Nemirovski.
Robust modeling of multi-stage portfolio problems.
In High performance optimization, pages 303-328. Kluwer Acad.
Publ., Dordrecht, 2000.
Aharon Ben-Tal, Tamar Margalit, and Arkadi Nemirovski.
Robust modeling of multi-stage portfolio problems.
In High performance optimization, volume 33 of Appl.
Optim., pages 303-328. Kluwer Acad. Publ., Dordrecht, 2000.
Aharon Ben-Tal and Marc Teboulle.
The duality between expected utility and penalty in stochastic linear
programming.
In Stochastic programming (Gargnano, 1983), volume 76 of
Lecture Notes in Control and Inform. Sci., pages 151-161. Springer, Berlin,
1986.
Aharon Ben-Tal and Marc Teboulle.
Expected utility, penalty functions, and duality in stochastic
nonlinear programming.
Management Sci., 32(11):1445-1466, 1986.
Aharon Ben-Tal and Marc Teboulle.
Portfolio theory for the recourse certainty equivalent maximizing
investor.
Ann. Oper. Res. 31, 479-499, 1991.
P. Benedek, A. Darazs, and J.D. Pinter.
Risk management of accidental water pollution: An illustrative
application.
Water Science and Technology, 22:265-274, 1990.
Viktor Benes.
Conservative expectation estimators by means of an optimization
moment problem.
Ekon.-Mat. Obz. 21, 407-417, 1985.
A. Benveniste, P. Bernhard, and G. Cohen.
On the decomposition of stochastic control problems.
1rst IFAC Symposium on Large Scale Systems Theory and
Applications, Udine, Italie, 1976.
A. Benveniste and P. Crepel.
Normalite asymptotique: Approximation diffusion pour les algorithmes
recursifs: Et applications.
Cah. Cent. Etud. Rech. Oper. 23, 3-26, 1981.
Albert Benveniste.
Introduction a la methode de l'equation differentielle moyenne pour
l'etude des algorithmes recursifs. Exemples.
Cah. Cent. Etud. Rech. Oper. 22, 219-241, 1980.
Regina Benveniste.
A note on the set covering problem.
J. Oper. Res. Soc., 33(3):261-265, 1982.
P. Beraldi and A. Ruszczynski.
A branch and bound method for stochastic integer problems under
probabilistic constraints.
Optimization Methods and Software, 17(3):359-382, 2002.
Patrizia Beraldi, Roberto Musmanno, and Chefi Triki.
Solving stochastic linear programs with restricted recourse using
interior point methods.
Comput. Optim. Appl., 15(3):215-234, 2000.
Patrizia Beraldi, Roberto Musmanno, and Chefi Triki.
Solving stochastic linear programs with restricted recourse using
interior point methods.
Comput. Optim. Appl., 15(3):215-234, 2000.
Patrizia Beraldi and Andrzej Ruszczy\'nski.
The probabilistic set-covering problem.
Oper. Res., 50(6):956-967, 2002.
Patrizia Beraldi and Andrzej Ruszczy\'nski.
Beam search heuristic to solve stochastic integer problems under
probabilistic constraints.
European J. Oper. Res., 167(1):35-47, 2005.
Rustem Berc.
Robust optimal policy methods for nonlinear models.
In Operations research proceedings 1990, Pap. 19th Annu. Meet.
DGOR, Vienna/Austria 1990, 262-271, 1992.
B. Bereanu.
On stochastic linear programming.
Comun. Acad. Republ. popul. Romine 13, 517-522, 1963.
B. Bereanu.
Stochastic transportation problem. I: Random costs.
Comun. Acad. Republ. popul Romine 13, 325-331, 1963.
B. Bereanu.
Stochastic transportation problem. II: Random consumptions.
Comun. Acad. Republ. popul. Romine 13, 333-337, 1963.
B. Bereanu.
On the distribution of the optimum in stochastic linear
programming.
An. Univ. Bucuresti, Ser. Sti. Natur., Mat.-Mec. 14, No.2,
41-47, 1965.
B. Bereanu.
Renewal processes and some stochastic programming problems in
economics.
SIAM J. Appl. Math. 19, 308-322, 1970.
B. Bereanu.
Programmation stochastique et quelques-unes de ses applications
économiques.
Publ. Économétriques, 5:143-161, 1972.
B. Bereanu.
On the use of computers in planning under conditions of
uncertainty.
Computing 15, 11-32, 1975.
B. Bereanu.
The continuity of the optimum in parametric programming and
applications to stochastic programming.
J. Optimization Theory Appl., 18(3):319-333, 1976.
B. Bereanu.
Linear optimization with mixed indeterminacies in data.
Stud. Cerc. Mat., 33(6):601-610, 1981.
B. Bereanu.
Minimum risk criterion in stochastic optimization.
Econom. Comput. Econom. Cybernet. Stud. Res., 15(2):31-39,
1981.
B. Bereanu and G. Peeters.
A "wait-and-see" problem in stochastic linear programming. An
experimental computer code.
Cah. Cent. Etud. Rech. Oper. 12, 133-148, 1970.
Bernard Bereanu.
Quasi-convexity, strictly quasi-convexity and pseudo-convexity of
composite objective functions.
Rev. Française Automat. Informat. Recherche
Opérationnelle, 6(R-1):15-26, 1972.
Bernard Bereanu.
The Cartesian integration method in stochastic linear programming.
In Numerische Methoden bei Optimierungsaufgaben (Tagung, Math.
Forschungsinst., Oberwolfach, 1971), pages 9-20. Internat. Schriftenreihe
Numer. Math., Band 17, Basel, 1973. Birkhäuser.
Bernard Bereanu.
On stochastic linear programming. IV. Some numerical methods
and their computer implementation.
In Proceedings of the Fourth Conference on Probability Theory
(Bra sov, 1971), pages 13-36. Editura Acad. R.S.R., Bucharest, 1973.
Bernard Bereanu.
The Cartesian integration method in stochastic linear programming.
In Numer. Meth. Optimierungsaufg., Tagung Oberwolfach 1971,
ISNM 17, 9-20 , 1973.
Bernard Bereanu.
Stable stochastic linear programs and applications.
Math. Operationsforsch. Statist., 6(4):593-607, 1975.
Bernard Bereanu.
The generalized distribution problem of stochastic linear
programming.
In Symposia Mathematica, Vol. XIX (Convegno sulla Programmazione
Matematica e sue Applicazioni, INDAM, Roma, 1974), pages 229-267, London,
1976. Academic Press.
Bernard Bereanu.
On some distribution-free, optimal solutions/bases, in stochastic
linear programming.
Rev. Roumaine Math. Pures Appl., 21(6):643-657, 1976.
Bernard Bereanu.
A unifying approach to stochastic linear programming.
In Proc. 5th Conf. Probab. Theory, Brasov 1974, 23-31, 1977.
Bernard Bereanu.
Stochastic-parametric linear programs. I.
Revue Roumaine Math. Pur. Appl. 22, 1367-1380, 1977.
Bernard Bereanu.
Stochastic-parametric linear programs. I.
Rev. Roumaine Math. Pures Appl., 22(10):1367-1380, 1977.
Bernard Bereanu.
Some numerical methods in stochastic linear programming under risk
and uncertainty.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 169-205. Academic Press, London, 1980.
Bernard Bereanu.
Stochastic-parametric linear programs. II.
In Recent results in stochastic programming (Proc. Meeting,
Oberwolfach, 1979), volume 179 of Lecture Notes in Econom. and Math.
Systems, pages 3-20. Springer, Berlin, 1980.
Bernard Bereanu.
An approach to complexity of optimization linear models.
In Proceedings of the Sixth Conference on Probability Theory
(Bra sov, 1979), pages 385-394, Bucharest, 1981. Ed. Acad. R.S.
România.
Bernard Bereanu.
Programming with composite indeterminacies in data.
Methods Oper. Res. 41, 117-120, 1981.
Bernard Bereanu.
Programming with objective and conventional perturbation in data.
In Studies in probability and related topics, pages 21-38.
Nagard, Rome, 1983.
S.A. Berezin and B.L. Lavrovskij.
Probability models of optimization. Textbook. (Veroyatnostnye
modeli optimizatsii. Uchebnoe posobie).
Ministerstvo Vysshego i Srednego Spetsial'nogo Obrazovaniya RSFSR.
Novosibirskij Gosudarstvennyj Universitet im. Leninskogo Komsola.
Novosibirsk: Novosibirskij Gosudarstvennyj Universitet., 1980.
V.A. Bereznev.
A stochastic programming problem with probabilistic constraints.
Engrg. Cybernetics, 9(4):613-619 (1972), 1971.
V.A. Bereznev.
Zwei stochastische Modelle der Planung der Produktion von
Halbfabrikaten.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1975, Nr. 1, 26-31, 1975.
Menachem Berg.
Optimal replacement policies for two-unit machines with increasing
running costs. I.
Stochastic Processes Appl. 4, 89-106, 1976.
Adam J. Berger, John M. Mulvey, and Andrzej Ruszczy\'nski.
An extension of the DQA algorithm to convex stochastic
programs.
SIAM J. Optim., 4(4):735-753, 1994.
Adam J. Berger, John M. Mulvey, and Andrzej Ruszczy\'nski.
Restarting strategies for the DQA algorithm.
In Large scale optimization (Gainesville, FL, 1993), pages
1-25. Kluwer Acad. Publ., Dordrecht, 1994.
J.O. Berger and G. Salinetti.
Approximations of Bayes decision problems: the epigraphical
approach.
Ann. Oper. Res., 56:1-13, 1995.
Stochastic programming (Udine, 1992).
H. Berglann and S. D. Flåm.
Stochastic approximation, momentum, and Nash play.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 337-345. SIAM, Philadelphia, PA, 2005.
Milan Berka.
Stochastic methods for global optimization of dynamical systems.
Kniznice Odborn. Ved. Spis\.u Vysoké. Ucení\
Tech. v Brne B, 111:7-23, 1986.
Arjan Berkelaar, Cees Dert, Bart Oldenkamp, and Shuzhong Zhang.
A primal-dual decomposition-based interior point approach to
two-stage stochastic linear programming.
Oper. Res., 50(5):904-915, 2002.
Arjan Berkelaar, Joaquim A. S. Gromicho, Roy Kouwenberg, and Shuzhong Zhang.
A primal-dual decomposition algorithm for multistage stochastic
convex programming.
Math. Program., 104(1, Ser. A):153-177, 2005.
E.M. Berkovic.
The approximation of two-stage stochastic extremal problems.
U.S.S.R. Comput. Math. math. Phys. 1 Nr. 5, 69-88 (1973).,
1971.
E.M. Berkovic.
Differenzenapproximationen fuer Zweietappenprobleme der
stochastischen optimalen Steuerung.
Vestnik Moskov. Univ., Ser. I 27, No.3. 43-51, 1972.
E.M. Berkovic.
Ueber Existenzsaetze in Zweietappenproblemen der stochastischen
optimalen Steuerung.
Vestnik Moskov. Univ., Ser. I 27, No.2, 64-70, 1972.
E.M. Berkovich.
The approximation of two-stage stochastic extrem problems.
Zh. vycislit. Mat. Mat. Fiz. 11, 1150-1165, 1971.
E.M. Berkovich and M.I. Ron'kin.
Mathematical models for optimization of reference information
services.
Autom. Doc. Math. Linguist. 23, No.6, 14-21 translation from
Nauchno-Tekh. Inf., Ser. 2 1989, No.11, 19-23 (1989)., 1989.
Nils Jacob Berland and Kjetil K. Haugen.
Mixing stochastic dynamic programming and scenario aggregation.
Ann. Oper. Res., 64:1-19, 1996.
Stochastic programming, algorithms and models (Lillehammer, 1994).
Oded Berman, Zvi Ganz, and Janet M. Wagner.
A stochastic optimization model for planning capacity expansion in a
service industry under uncertain demand.
Nav. Res. Logist. 41, No.4, 545-564, 1994.
Oded Berman and David Simchi-Levi.
Finding the optimal a priori tour and location of a traveling
salesman with nonhomogeneous customers.
Transp. Sci. 22, No.2, 148-154, 1988.
W. Bernard and B. Hiller.
Spektralanalyse als Identifikationsproblem. I.
Regelungstechnik 30, 393-398, 1982.
W. Bernard and B. Hiller.
Spektralanalyse als Identifikationsproblem. II.
Regelungstechnik 30, 422-427, 1982.
Siegfried Berninghaus.
Das "Multi-Armed-Bandit"-Paradigma.
Mathematical Systems in Economics, 92. Koenigstein/Ts.: Verlagsgruppe
Athenaeum/Hain/Hanstein., 1984.
Marida Bertocchi, Vittorio Moriggia, and Jitka Dupacová.
Horizon and stages in applications of stochastic programming in
finance.
Ann. Oper. Res., 142:63-78, 2006.
Dimitri P. Bertsekas and John N. Tsitsiklis.
An analysis of stochastic shortest path problems.
Math. Oper. Res. 16, No.3, 580-595, 1991.
D.P. Bertsekas.
Stochastic optimization problems with nondifferentiable cost
functionals.
J. Optimization Theory Appl. 12, 218-231, 1973.
Dimitris Bertsimas, Leonid Kogan, and Andrew W. Lo.
Hedging derivative securities and incomplete markets: an
e-arbitrage approach.
Oper. Res., 49(3):372-397, 2001.
Dimitris Bertsimas and José Niño-Mora.
Restless bandits, linear programming relaxations, and a primal-dual
index heuristic.
Oper. Res., 48(1):80-90, 2000.
Dimitris Bertsimas and Melvyn Sim.
Tractable approximations to robust conic optimization problems.
Math. Program., 107(1-2, Ser. B):5-36, 2006.
Dimitris Bertsimas, Chungpiaw Teo, and Rakesh Vohra.
On dependent randomized rounding algorithms.
Oper. Res. Lett., 24(3):105-114, 1999.
Dimitris Bertsimas and Aurélie Thiele.
A robust optimization approach to supply chain management.
In Integer programming and combinatorial optimization, volume
3064 of Lecture Notes in Comput. Sci., pages 86-100. Springer, Berlin,
2004.
B. Betro' and L. De Biase.
A Newton-like method for stochastic optimization.
In Towards global optimisation 2, Workshops Varenna 1976 and
Bergamo 1977, 269-289, 1978.
B. Betrò and F. Schoen.
Optimal and sub-optimal stopping rules for the multistart algorithm
in global optimization.
Math. Programming, 57(3, Ser. A):445-458, 1992.
Bruno Betrò and Alessandra Guglielmi.
Methods for global prior robustness under generalized moment
conditions.
In Robust Bayesian analysis, pages 273-293. Springer, New
York, 2000.
Bruno Betrò and Fabio Schoen.
Sequential stopping rules for the multistart algorithm in global
optimisation.
Math. Programming, 38(3):271-286, 1987.
Bruno Betrò and Fabio Schoen.
A stochastic technique for global optimization.
Comput. Math. Appl., 21(6-7):127-133, 1991.
B. Bharath and V. S. Borkar.
Robust parameter optimization of hidden Markov models.
J. Indian Inst. Sci., 78(2):119-130, 1998.
B. Bharath and V. S. Borkar.
Stochastic approximation algorithms: overview and recent trends.
S¯adhan¯a, 24(4-5):425-452, 1999.
Chance as necessity.
Kabekode V.S. Bhat.
Minimization of disjunctive normal forms of fuzzy logic functions.
J. Franklin Inst., 311(3):171-185, 1981.
S. Bhatnagar, M.C. Fu, and S.I. Marcus.
Rate-based ABR flow control using two timescale SPSA.
In Proceedings of the SPIE, volume 3841, pages 142-149. The
International Society for Optical Engineering, 1999.
Sandeep N. Bhatt, Fan R.K. Chung, and Arnold L. Rosenberg.
Partitioning circuits for improved testability.
Algorithmica, 6(1):37-48, 1991.
Malay Bhattacharyya.
Joint randomized decisions in chance-constrained programming.
J. Oper. Res. Soc. 35, 355-357, 1984.
Malay Bhattacharyya.
Superiority of randomised decisions in chance constrained programming
(CCP).
Bull. Inst. Internat. Statist., 52(2):391-406, 1987.
Siddhartha Bhattacharyya and Marvin D. Troutt.
Genetic search over probability spaces.
European J. Oper. Res., 144(2):333-347, 2003.
Horst Bialy and Michael Olbrich.
Optimierung. Eine Einfuehrung mit Anwendungsbeispielen.
Mathematik fuer Ingenieure. Leipzig: VEB Fachbuchverlag., 1975.
D. Bienstock and J.F. Shapiro.
Optimizing resource acquisition decisions by stochastic programming.
Management Science, 34(2):215-229, 1988.
S. \.Ilker Birbil, Gül Gürkan, and Ovidiu Liste s.
Solving stochastic mathematical programs with complementarity
constraints using simulation.
Math. Oper. Res., 31(4):739-760, 2006.
J. R. Birge, N. C. P. Edirisinghe, and W. T. Ziemba, editors.
Research in stochastic programming.
Kluwer Academic Publishers, Dordrecht, 2001.
Papers from the 8th International Conference on Stochastic
Programming held at the University of British Columbia, Vancouver, BC, August
8-16, 1998, Ann. Oper Res. 100 (2000).
J. R. Birge, L. Qi, and Z. Wei.
Convergence analysis of some methods for minimizing a nonsmooth
convex function.
J. Optim. Theory Appl., 97(2):357-383, 1998.
J. R. Birge and C. H. Rosa.
Modeling investment uncertainty in the costs of global CO2 emission
policy.
European Journal of Operational Research, 83(3):466-488, 1995.
J. R. Birge, S. Takriti, and E. Long.
Intelligent unified control of unit commitment and generation
allocation.
Technical Report 94-26 (revised 1995), Department of Industral and
Operations Engineering, The University of Michigan, Ann Arbor, 1994.
John Birge and Marc Teboulle.
Upper bounds on the expected value of a convex function using
gradient and conjugate function information.
Math. Oper. Res., 14(4):745-759, 1989.
John R. Birge.
The value of the stochastic solution in stochastic linear programs
with fixed recourse.
Math. Programming, 24(3):314-325, 1982.
John R. Birge.
Aggregation bounds in stochastic linear programming.
Math. Programming, 31(1):25-41, 1985.
John R. Birge.
Decomposition and partitioning methods for multistage stochastic
linear programs.
Oper. Res., 33(5):989-1007, 1985.
John R. Birge.
Stochastic programming: optimizing the uncertain.
In Optimization, Vol. 1, 2 (Singapore, 1992), pages 613-632.
World Sci. Publishing, River Edge, NJ, 1992.
John R. Birge.
Models and model value in stochastic programming.
Ann. Oper. Res., 59:1-18, 1995.
Models for planning under uncertainty.
John R. Birge.
Stochastic programming computation and applications.
INFORMS J. Comput., 9(2):111-133, 1997.
John R. Birge and M.A.H. Dempster.
Optimal match-up strategies in stochastic scheduling.
Discrete Appl. Math. 57, No.2-3, 105-120, 1995.
John R. Birge and M.A.H. Dempster.
Stochastic programming approaches to stochastic scheduling.
J. Global Optim., 9(3-4):417-451, 1996.
Optimization applications in scheduling theory.
John R. Birge and José H. Dulá.
Bounding separable recourse functions with limited distribution
information.
Ann. Oper. Res., 30(1-4):277-298, 1991.
Stochastic programming, Part I (Ann Arbor, MI, 1989).
John R. Birge and François Louveaux.
Introduction to stochastic programming.
Springer-Verlag, New York, 1997.
John R. Birge and François V. Louveaux.
A multicut algorithm for two-stage stochastic linear programs.
European J. Oper. Res., 34(3):384-392, 1988.
John R. Birge, Stephen M. Pollock, and Liqun Qi.
A quadratic recourse function for the two-stage stochastic program.
In Progress in optimization (Perth, 1998), pages 109-121.
Kluwer Acad. Publ., Dordrecht, 2000.
John R. Birge and Li Qun Qi.
Computing block-angular Karmarkar projections with applications to
stochastic programming.
Management Sci., 34(12):1472-1479, 1988.
John R. Birge and Li Qun Qi.
Semiregularity and generalized subdifferentials with applications to
optimization.
Math. Oper. Res., 18(4):982-1005, 1993.
John R. Birge and Li Qun Qi.
Continuous approximation schemes for stochastic programs.
Ann. Oper. Res., 56:15-38, 1995.
Stochastic programming (Udine, 1992).
John R. Birge and Li Qun Qi.
Subdifferential convergence in stochastic programs.
SIAM J. Optim., 5(2):436-453, 1995.
John R. Birge and Charles H. Rosa.
Parallel decomposition of large-scale stochastic nonlinear programs.
Ann. Oper. Res., 64:39-65, 1996.
Stochastic programming, algorithms and models (Lillehammer, 1994).
John R. Birge and Robert L. Smith.
Random procedures for nonredundant constraint identification in
stochastic linear programs.
Amer. J. Math. Management Sci., 4(1-2):41-70, 1984.
Statistics and optimization : the interface.
John R. Birge and Samer Takriti.
Successive approximations of linear control models.
SIAM J. Control Optim., 37(1):165-176 (electronic), 1999.
John R. Birge and Stein W. Wallace.
Refining bounds for stochastic linear programs with linearly
transformed independent random variables.
Oper. Res. Lett., 5(2):73-77, 1986.
John R. Birge and Stein W. Wallace.
A separable piecewise linear upper bound for stochastic linear
programs.
SIAM J. Control Optim., 26(3):725-739, 1988.
John R. Birge and Roger J.-B. Wets.
Approximations and error bounds in stochastic programming.
In Inequalities in statistics and probability (Lincoln, Neb.,
1982), volume 5 of IMS Lecture Notes-Monograph Ser., pages 178-186,
Hayward, Calif., 1984. Inst. Math. Statist.
John R. Birge and Roger J.-B. Wets.
Designing approximation schemes for stochastic optimization problems,
in particular for stochastic programs with recourse.
Math. Programming Stud., 27:54-102, 1986.
Stochastic programming 84. I.
John R. Birge and Roger J.-B. Wets.
Computing bounds for stochastic programming problems by means of a
generalized moment problem.
Math. Oper. Res., 12(1):149-162, 1987.
John R. Birge and Roger J.-B. Wets.
Sublinear upper bounds for stochastic programs with recourse.
Math. Programming (Ser. A), 43(2):131-149, 1989.
J.R. Birge.
An L-shaped method computer code for multistage stochastic linear
programs.
In Numerical techniques for stochastic optimization, Springer
Ser. Comput. Math. 10, 255-266, 1988.
J.R. Birge.
Exhaustible resource models with uncertain returns from exploration
investment.
In Numerical techniques for stochastic optimization, Springer
Ser. Comput. Math. 10, 481-488, 1988.
J.R. Birge.
The relationship between the L-shaped method and dual basis
factorization for stochastic linear programming.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 267-272. Springer, Berlin, 1988.
J.R. Birge and C.J. Donohue.
An upper bound on the expected value of a non-increasing convex
function with convex marginal return functions.
Operations Research Letters 18:213-221, 1996.
J.R. Birge, C.J. Donohue, D.F. Holmes, and O.G. Svintsitski.
A parallel implementation of the nested decomposition algorithm for
multistage stochastic linear programs.
Mathematical Programming, 75(2):327-352, 1996.
J.R. Birge and D.F. Holmes.
Efficient solution of two-stage stochastic linear programs using
interior point methods.
Comput. Optim. Appl., 1(3):245-276, 1992.
J.R. Birge and C.H. Rosa.
Incorporating investment uncertainty into greenhouse policy models.
The Energy Journal 17:79-90, 1996.
J.R. Birge and C.H. Rosa.
Parallel decomposition of large scale stochastic nonlinear programs.
Annals of Operations Research, 64:39-65, 1996.
J.R. Birge and R.J.-B. Wets, editors.
Stochastic programming. Part I.
J.C. Baltzer A.G., Basel, 1991.
Papers from the Fifth International Conference held at the University
of Michigan, Ann Arbor, Michigan, August 13-18, 1989, Ann. Oper. Res.
30 (1991), no. 1-4.
J.R. Birge and R.J.-B. Wets, editors.
Stochastic programming. Part II.
J.C. Baltzer A.G., Basel, 1991.
Papers from the Fifth International Conference held at the University
of Michigan, Ann Arbor, Michigan, August 13-18, 1989, Ann. Oper. Res.
31 (1991), no. 1-4.
M. P. Biswal, N. P. Sahoo, and Duan Li.
Probabilistic linearly constrained programming problems with
lognormal random variables.
Opsearch, 42(1):70-76, 2005.
Gabriel R. Bitran, Elizabeth A. Haas, and Hirofumi Matsuo.
Production planning of style goods with high setup costs and
forecast revisions.
Oper. Res. 34, 226-236, 1986.
G.R. Bitran and D. Tirupati.
Hierarchical production planning.
In S.C. Graves, A.H.G. Rinnooy Kan, and P.H. Zipkin, editors,
Handbooks on Operations Research and Management Science, volume 4, pages
523-568. North-Holland, Amsterdam, 1993.
H. Bjørstad, D. Haugland, and R. Helming.
A stochastic model for gasoline blending.
In M. Breton and G. Zaccour, editors, Advances in Operations
Research in the Oil and Gas Industry, pages 137-142, 1991.
Charles Blair.
Random inequality constraint systems with few variables.
Math. Programming, 35(2):135-139, 1986.
Roger A. Blau.
Erratum: N job, one machine sequencing problems under uncertainty.
Management Sci., Theory 20, 896-899, 1974.
Roger A. Blau.
Stochastic programming and decision analysis: an apparent dilemma.
Management Sci., 21(3):271-276, 1974.
Jean-Marie Blin, King-Sun Fu, Andrew B. Whinston, and Kenneth B. Moberg.
Pattern recognition in micro-economics.
J. Cybernetics 3, Nr. 4, 17-27, 1973.
Jörgen Blomvall and Per Olov Lindberg.
A Riccati-based primal interior point solver for multistage
stochastic programming.
European J. Oper. Res., 143(2):452-461, 2002.
Interior point methods (Budapest, 2000).
Jörgen Blomvall and Per Olov Lindberg.
A Riccati-based primal interior point solver for multistage
stochastic programming-extensions.
Optim. Methods Softw., 17(3):383-407, 2002.
Stochastic programming.
Jörgen Blomvall and Alexander Shapiro.
Solving multistage asset investment problems by the sample average
approximation method.
Math. Program., 108(2-3, Ser. B):571-595, 2006.
J. A. Bloom.
Solving an electricity generation capacity expansion planning problem
by generalized Benders' decomposition.
Operations Research, 31:84-100, 1983.
Lawrence Blume, David Easley, and Maureen O'Hara.
Characterization of optimal plans for stochastic dynamic programs.
J. Econ. Theory 28, 221-234, 1982.
John Board, Charles Sutcliffe, and William T. Ziemba.
The application of operations research techniques to financial
markets.
Stochastic Programming E-Print Series, http://www.speps.org,
1999.
I.I. Bockareva.
The algorithm for the solution of a certain class of stochastic
programming problems with probabilistic constraints.
Optimal. Planirovanie, 16:10-15, 1970.
C.E. Bocvarova.
Optimierung der Struktur eines Flaecheninhalts fuer verschiedene
Kulturen auf einer Flaeche ohne Bedarf an Bewaesserung und auf einem
vorgegebenen Massiv, der aus einer nichteinregulierten Quelle bei
stochastischen Schwankungen der Wasserverhaeltnisse bewaessert wird.
Optimizacija 17(34), 67-72, 1975.
C.E. Bocvarova.
Ueber die Wahl der garantierten Wahrscheinlichkeiten in Problemen
der stochastischen Programmierung mit Wahrscheinlichkeitsbeschraenkungen.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1975, Nr. 6, 47-54, 1975.
C.E. Bocvarova.
Ueber ein stochastisches Modell der Bestimmung der Optimalzahl der
staendig beschaeftigten Arbeiter und der Struktur der Produktion eines
landwirtschaftlichen Unternehmens.
Optimizacija 16(33), 73-83, 1975.
C. Guus E. Boender and H. Edwin Romeijn.
Stochastic methods.
In Handbook of global optimization, volume 2 of Nonconvex
Optim. Appl., pages 829-869. Kluwer Acad. Publ., Dordrecht, 1995.
C.G.E. Boender and A.H.G. Rinnooy Kan.
Bayesian stopping rules for multistart global optimization methods.
Math. Programming, 37(1):59-80, 1987.
C.G.E. Boender, A.H.G. Rinnooy Kan, L. Stougie, and G.T. Timmer.
Global optimization: a stochastic approach.
In Numerical techniques for stochastic systems (Conf., Gargnano,
1979), pages 387-394, Amsterdam, 1980. North-Holland.
C.G.E. Boender, A.H.G. Rinnooy Kan, and C. Vercellis.
Stochastic optimization methods.
In Stochastics in combinatorial optimization (Udine, 1986),
pages 94-112. World Sci. Publishing, Singapore, 1987.
Juergen Boettcher.
Local decomposition for linear programs with dual block-angular
structure.
Methods Oper. Res. 58, 15-25, 1989.
Alexander Bofinger.
Numerical determination of response surface points with minimal
absolute value for a lower bound estimate of multinormal integrals-gradient
algorithms and their convergence.
In Stochastic optimization techniques (Neubiberg/Munich, 2000),
volume 513 of Lecture Notes in Econom. and Math. Systems, pages 59-78.
Springer, Berlin, 2002.
I.P. Boglaev.
Finding a feasible optimal program in the problem of programmed
control under disturbances.
Autom. Remote Control 43, 1315-1321 translation from Avtom.
Telemekh. 1982, No.10, 107-114 (1982)., 1983.
M.G.V. Bogle and M.J. O'Sullivan.
The effect of storage carryover on plant expansion planning.
J. Oper. Res. Soc. 31, 319-324, 1980.
V.A. Bol'shakov.
Local-optimization algorithms of group supplies control.
Podstawy Sterowania 13, 165-176, 1983.
P. Bonami and M.A. Lejeune.
An exact solution approach for portfolio optimization problems under
stochastic and integer constraints.
Stochastic Programming E-Print Series, http://www.speps.org,
2007.
I.M. Bondar'.
Application of stochastic programming methods in the construction of
mathematical models of complex systems under conditions of incomplete
information.
In Methods of operations research and of reliability theory in
systems analysis (Russian), pages 52-63, Kiev, 1977. Akad. Nauk Ukrain. SSR
Inst. Kibernet.
V.A. Bondarenko and A.A. Korotkin.
Analysis of discrete optimization algorithms using incomplete
information.
Zh. Vychisl. Mat. i Mat. Fiz., 21(3):783-786, 814, 1981.
J.G. Boon, J.D. Pinter, and L. Somlyody.
A new stochastic approach for controlling point source river
pollution.
In Proc. 3rd Scientific Assembly of the International
Association for Hydrologic Sciences (Baltimore, 1989), pages 241-249. IAHS
Publications 180, 1989.
A. E. Bopp, V. R. Kannan, S. W. Palocsay, and S. P. Stevens.
An optimization model for planning natural gas purchases,
transportation, storage and deliverability.
Omega, International Journal of Management Science,
24(5):511-522, 1996.
A.N. Bordunov, A.I. Yastremskij, and N. Utenliev.
On duality relations and optimality conditions in linear problems of
stochastic programming with discretely distributed random variables.
Issled. Oper. ASU 29, 27-37, 1987.
N.N. Bordunov.
Optimality conditions for multistep control of stochastic convex
mappings.
Cybernetics, 19(1):48-56, 1983.
N.N. Bordunov.
Dynamic programming and conditions of optimality of control of
stochastic convex mappings.
Cybernetics, 20(2):233-239, 1984.
N.N. Bordunov.
Extremum conditions for a multistage problem of stochastic
programming with smooth constraints.
Kibernetika (Kiev), 6:iii, 77-82, 135, 1984.
N.N. Bordunov.
Convex multivalued mappings and stochastic models of the dynamics of
economic systems.
In Stochastic optimization, Proc. Int. Conf., Kiev/USSR 1984,
Lect. Notes Control Inf. Sci. 81, 309-313, 1986.
N.N. Bordunov.
Dynamical optimization models under conditions of uncertainty:
multistage problems of stochastic programming (from IIASA reports).
(Dinamicheskie optimizatsionnye modeli v usloviyakh neopredelennosti:
mnogoehtapnye zadachi stokhasticheskogo programmirovaniya (po materialam
IIASA)).
Institut Kibernetiki AN USSR, Kiev, 1991.
N.N. Bordunov, N. Uteuliev, and A.I. Yastremskii.
Duality relations and conditions for optimality in nonlinear
stochastic programming problems.
Issled. Operatsii i ASU, 31:19-22, 112, 1988.
N.N. Bordunov, N. Uteuliev, and A.I. Yastremskii.
Multistage problems in stochastic linear programming with discrete
distributions.
Vestnik Kiev. Univ. Model. Optim. Slozhn. Sist., 7:90-95, 109,
1988.
N.N. Bordunov and N. Uteuliyev.
The subdifferential form of optimality conditions for convex
multistage stochastic programming problems.
Soviet J. Automat. Inform. Sci., 21(3):52-56 (1989), 1988.
N.N. Bordunov, A.I. Yastremskii, and N. Uteuliev.
Duality relations and conditions for optimality in linear problems of
stochastic programming with discretely distributed random variables.
Issled. Operatsii i ASU, 29:27-37, 114, 1987.
N.N. Bordunov, A.I. Yastremskii, and N. Uteuliev.
Stochastic programming problems with discrete distributions.
Kibernetika (Kiev), 3:121-122, 135, 1988.
Karl-Heinz Borgwardt.
Untersuchungen zur Asymptotik der mittleren Schrittzahl von
Simplexverfahren in der linearen Optimierung., 1977.
A.I. Borisenko, Ya.I. Zelyk, V.M. Kuntsevich, and M.M. Lychak.
The convergence of a matrix stochastic control algorithm for a
static object with constrained controls.
Sov. J. Autom. Inf. Sci. 18, No.2, 41-49 translation from
Avtomatika 1985, No.2, 43-51 (1985)., 1985.
A. B. Borison, P. A. Morris, and S. S. Oren.
A state-of-the-world decomposition approach to dynamics and
uncertainty in electric utility generation expansion planning.
Operations Research, 32:1052-1068, 1984.
Vivek S. Borkar.
Pathwise recurrence orders and simulated annealing.
J. Appl. Probab., 29(2):472-476, 1992.
N.I. Borodyanskii.
Limit theorems for some problems of stochastic optimization.
Issled. Operatsii i ASU, 23:16-19, 138, 1984.
A.V. Borshchevskii.
On the problem of identification of parameters of nonlinear models by
the method of least absolute values.
In Investigation of methods for solving extremal problems
(Russian), pages 3-7, i, Kiev, 1986. Akad. Nauk Ukrain. SSR Inst. Kibernet.
M.Z. Borshchevskij, I.V. German, and L.M. Shevchuk.
Multistep process of taking decisions under conditions of
uncertainty.
In Approximate methods in the solution of operator equations
and their applications, Collect. Artic., Irkutsk 1982, 182-193, 1982.
Jürgen Böttcher.
Stochastische lineare Programme mit Kompensation, volume
115 of Mathematical Systems in Economics.
Athenäum Verlag GmbH, Frankfurt am Main, 1989.
M. Bouhadi, R. Ellaia, and J. E. Souza de Cursi.
Stochastic perturbation methods for affine restrictions.
In Advances in convex analysis and global optimization
(Pythagorion, 2000), volume 54 of Nonconvex Optim. Appl., pages
487-499. Kluwer Acad. Publ., Dordrecht, 2001.
E.-K. Boukas, A. Haurie, and P. Michel.
An optimal control problem with a random stopping time.
J. Optim. Theory Appl., 64(3):471-480, 1990.
Craig Boutilier, Richard Dearden, and Moisés Goldszmidt.
Stochastic dynamic programming with factored representations.
Artificial Intelligence, 121(1-2):49-107, 2000.
C. Bouza, editor.
Stochastic Programming: The State of the Art.
1993.
Revista Investigación Operational 14, No.2-3.
Carlos Bouza.
Bootstrap estimation of the bound of the approximation error in
stochastic programming with complete fixed recourse.
Investigación Oper., 13(3):226-232, 1992.
Carlos Bouza.
Approximation of the value of the optimal solution in stochastic
programming.
Investigación Oper., 15(1):13-33, 1994.
Carlos Bouza and Sira Allende.
Use of L-estimate approximations for solving chance constrained
programs.
Investigación Oper., 14(2-3):119-126, 1993.
Workshop on Stochastic Optimization: the State of the Art (Havana,
1992).
Carlos Bouza Herrera.
Bounding the expected approximation error in stochastic linear
programming with complete fixed recourse.
In System modelling and optimization (Zurich, 1991), volume 180
of Lecture Notes in Control and Inform. Sci., pages 541-545. Springer,
Berlin, 1992.
Carlos Bouza Herrera.
Use of bootstrap approximations for solving stochastic linear
programming problems.
Investigación Oper., 16(1-2):3-10, 1995.
Carlos Bouza Herrera and Sira Allende Alonso.
Density function estimation and the approximation of convergence
rates in stochastic linear programming.
Investigación Oper., 10(3):135-140, 1989.
Carlos Bouza Herrera and Sira Allende Alonso.
Bounding the variance of the approximation error.
Investigación Oper., 11(1):29-36, 1990.
Aziz Bouzaher.
Symmetric QP and linear programming under primal-dual uncertainty.
Oper. Res. Lett. 6, 221-225, 1987.
Aziz Bouzaher and Susan Offutt.
A stochastic linear programming model for corn residue production.
J. Oper. Res. Soc. 43, No.9, 843-857, 1992.
Bruce L. Bowerman and Anne B. Koehler.
An optimal policy for sampling from uncertain distributions.
Comm. Statist. A-Theory Methods, 7(11):1041-1051, 1978.
H. Woods Bowman and Anne Marie Laporte.
Stochastic optimization in recursive equation systems with random
parameters with an application to control of the money supply.
In Studies in Bayesian econometrics and statistics (in honor of
Leonard J. Savage), pages 441-462. Contributions to Economic Analysis, No.
86, North-Holland, Amsterdam, 1975.
Reprinted from Ann. Econom. Social Measurement 1 (1972),
419-435.
Friedrich Bozena and Klaus Tammer.
Untersuchungen zur Stabilitaet einiger Ersatzprobleme der
stochastischen Optimierung in bezug auf Aenderungen des zugrunde gelegten
Zufallsvektors.
Wiss. Z. Humboldt-Univ. Berlin, Math.-Naturwiss. Reihe 30,
373-376, 1981.
Steven J. Brams and D. Marc Kilgour.
The box problem: to switch or not to switch.
Math. Mag., 68(1):27-34, 1995.
Andries S. Brandsma and A.J. Hughes Hallett.
A method for optimising risk sensitive decisions.
Rev. Belg. Stat. Inf. Rech. Oper. 24, No.3, 30-43, 1983.
Andrzej Brandt, Stefan Jendo, and Wojciech Marks.
Probabilistic approach to reliability-based optimal structural
design.
Rozpr. Inz. 32, 57-74, 1984.
Andrzej M. Brandt, Wojciech Dzieniszewski, Stefan Jendo, Wojciech Marks, Stefan
Owczarek, and Zbigniew Wasiutynski.
Criteria and methods of structural optimization. Ed. by Andrzej
M. Brandt. Transl. from the Polish by Antoni Pol.
Dordrecht/Boston/Lancaster: Martinus Nijhoff Publishers, 1986.
C. Brasca, F. Romeo, R. Scattolini, and N. Schiavoni.
Reduced-order modelling of large-scale stochastic systems: a
two-level Newton-like algorithm.
In Control applications of nonlinear programming and
optimization, Coll. Pap., 2nd IFAC Workshop, Oberpfaffenhofen/Ger. 1980,
44-54, 1980.
Michèle Breton and Saeb El Hachem.
Algorithms for the solution of a large-scale single-controller
stochastic game.
In Advances in dynamic games and applications (Geneva, 1992),
volume 1 of Ann. Internat. Soc. Dynam. Games, pages 195-216.
Birkhäuser Boston, Boston, MA, 1994.
Michèle Breton and Saeb El Hachem.
Algorithms for the solution of stochastic dynamic minimax problems.
Comput. Optim. Appl., 4(4):317-345, 1995.
Michèle Breton and Saeb El Hachem.
A scenario aggregation algorithm for the solution of stochastic
dynamic minimax problems.
Stochastics Stochastics Rep., 53(3-4):305-322, 1995.
D.F. Broens and W.K. Klein Haneveld.
Investment evaluation based on the commercial scope. the production
of natural gas.
Annals of Operations Research, 59:195-226, 1995.
D.M. Brooks and C.T. Leondes.
Use of isolated forecasts in Markov decision processes with
imperfect information.
AIIE Trans., 6:244-251, 1974.
George W. Brown.
Recursive sets of rules in statistical decision processes.
In Statist. Papers in Honor of George W. Snedecor, 59-75,
1972.
Gerald G. Brown and Herbert C. Rutemiller.
Means and variances of stochastic vector products with applications
to random linear models.
Manage. Sci. 24, 210-216, 1977.
Lawrence D. Brown.
Closure theorems for sequential-design processes.
In Statistical decision theory and related topics II, Proc.
Symp., West Lafayette/Indiana 1976, 57-91, 1977.
Lawrence D. Brown and Bharat T. Doshi.
Existence of optimal policies in stochastic dynamic programming.
Probab. Math. Statist., 1(2):171-184 (1981), 1980.
R. Brunelli and G.P. Tecchiolli.
Stochastic minimization with adaptive memory.
J. Comput. Appl. Math., 57(3):329-343, 1995.
Giordano Bruno and Angelo Gilio.
Application of the simplex method to the fundamental theorem for the
probabilities in the subjective theory.
Statistica (Bologna), 40(3):337-344, 1980.
Noel A. Bryson and Saul I. Gass.
Solving discrete stochastic linear programs with simple recourse by
the dualplex algorithm.
Comput. Oper. Res. 21, No.1, 11-17, 1994.
Z. Bubnicki.
Optimization problems in large-scale systems modelling and
identification.
In Large scale systems: theory and applications, Proc.
IFAC/IFORS Symp., Warsaw/Pol. 1983, IFAC Proc. Ser. 10, 411-416, 1984.
Z. Bubnicki, M. Staro\'swiecki, and A. Lebrun.
Stochastic approach to the two-level optimization of the complex of
operations.
In Optimization techniques (Proc. Ninth IFIP Conf., Warsaw,
1979), Part 2, volume 23 of Lecture Notes in Control and Information
Sci., pages 281-290. Springer, Berlin, 1980.
C. S. Buchanan, K. I. M. McKinnon, and G. K. Skondras.
The recursive definition of stochastic linear programming problems
within an algebraic modeling language.
Ann. Oper Res., 104:15-32 (2002), 2001.
Modeling languages and systems.
J.J. Buckley.
Fuzzy programming and the Pareto optimal set.
Fuzzy Sets Syst. 10, 57-63, 1983.
J.J. Buckley.
Possibility and necessity in optimization.
Fuzzy Sets and Systems, 25(1):1-13, 1988.
J.J. Buckley.
Solving possibilistic linear programming problems.
Fuzzy Sets and Systems, 31(3):329-341, 1989.
J.J. Buckley.
Stochastic versus possibilistic multiobjective programming.
In Stochastic versus fuzzy approaches to multiobjective
mathematical programming under uncertainty, volume 6 of Theory Decis.
Lib. Ser. D System Theory Knowledge Engrg. Probl. Solving, pages 353-364.
Kluwer Acad. Publ., Dordrecht, 1990.
G. Buehler.
Informationsbedarf bei Produktions-Lagerhaltungs-Modellen.
In Proc. Oper. Res. 6, DGOR, Pap. ann. Meet. 1976, 380-389,
1976.
W. Buehler.
Ein kombiniertes Kompensations-Chance-Constrained Modell der
stochastischen linearen Programmierung.
In Operations Res.-Verf. 12, IV. Oberwolfach-Tag. Operations
Res. 1971, 61-68 , 1972.
W. Buehler.
Zur Loesung eines Zwei-Stufen-Risiko Modells der stochastischen
linearen Optimierung.
In Proc. Oper. Res., DGU Ann. Meet. 1971, 355-370, 1972.
Wolfgang Buehler.
Stochastische lineare Optimierung ueber halbgeordneten
Ereignismengen.
In Oper. Res. Verf. 21, VII. Oberwolfach-Tag. Oper. Res. 1974,
48-50, 1975.
Wolfgang Bühler.
Stochastische lineare Optimierung über halbgeordneten
Ereignismengen.
In Siebente Oberwolfach-Tagung über Operations Research
(1974), pages 48-50. Operations Research Verfahren, Band XXI, Meisenheim am
Glan, 1975. Hain.
József Bukszár.
Lower and upper bounds on the probability of the union of some events
with applications.
In Optimization theory (Mátraháza, 1999), volume 59 of
Appl. Optim., pages 33-43. Kluwer Acad. Publ., Dordrecht, 2001.
Jozsef Bukszar, René Henrion, Mihaly Hujter, and Tamas Szantai.
Polyhedral inclusion-exclusion.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
József Bukszár and András Prékopa.
Probability bounds with cherry trees.
Math. Oper. Res., 26(1):174-192, 2001.
József Bukszár and Tamás Szántai.
Probability bounds given by hypercherry trees.
Optim. Methods Softw., 17(3):409-422, 2002.
Stochastic programming.
D. W. Bulger, D. Alexander, W. P. Baritompa, G. R. Wood, and Z. B. Zabinsky.
Expected hitting times for backtracking adaptive search.
Optimization, 53(2):189-202, 2004.
D. W. Bulger and G. R. Wood.
Hesitant adaptive search for global optimisation.
Math. Programming, 81(1, Ser. A):89-102, 1998.
O. Bunke.
Einige Hilfsmittel zur Loesung statistischer Probleme bei der
linearen und nichtlinearen Optimierung.
Monatsber. Deutsch. Akad. Wiss. Berlin 8, 699-703, 1966.
O. Bunke.
Einige statistische Probleme und Methoden bei der linearen und
nichtlinearen Optimierung.
In Operationsforsch. Math. Statistik 1 (Schriftenr. Inst. Math.
Dtsch. Akad. Wiss. Berlin, Reihe B, H. 8), 19-34, 1968.
O. Bunke.
Ueber die Guete der linearen und nichtlinearen Optimierung bei
geschaetzten Parametern.
In Operationsforsch. Math. Statistik 1 (Schriftenr. Inst. Math.
Dtsch. Akad. Wiss. Berlin, Reihe B, H. 8), 9-18, 1968.
Derek W. Bunn and Spiros N. Paschentis.
A model-switching criterion for a class of stochastic linear
programs.
Appl. Math. Modelling, 5(5):336-340, 1981.
Derek W. Bunn and Spiros N. Paschentis.
Development of a stochastic model for the economic dispatch of
electric power.
Eur. J. Oper. Res. 27, 179-191, 1986.
Rainer E. Burkard and Helidon Dollani.
Robust location problems with pos/neg weights on a tree.
Networks, 38(2):102-113, 2001.
A. Burkauskas.
On convexity problems of the probabilistic constrained stochastic
programming problems.
Alkalmaz. Mat. Lapok, 12(1-2):77-90, 1986.
V.N. Burkov and D.A. Novikov.
Optimal stimulation mechanisms in an active system with probabilistic
uncertainty. II.
Avtomat. i Telemekh., 10:121-126, 1995.
F.V. Burshtein and È. S. Korelov.
Multicriteria decision making problems with indeterminacy and risk.
In Theoretical cybernetics (Russian), pages 143-148, Tbilisi,
1980. "Metsniereba".
K.V. Bury.
Design optimization under cost uncertainty and risk aversion.
INFOR, Canadian J. operat. Res. Inform. Processing 14, 250-258,
1976.
J. C. Butler and J. S. Dyer.
Optimizing natural gas flows with linear programming and scenarios.
Decision Sciences, 30(2):563-580, 1999.
Dan Butnariu.
Fixed points for fuzzy mappings.
Fuzzy Sets and Systems, 7(2):191-207, 1982.
Dan Butnariu.
Corrigendum: "Fixed points for fuzzy mappings" [Fuzzy Sets
and Systems 7 (1982), no. 2, 191-207; MR 83d:90150].
Fuzzy Sets and Systems, 12(1):93, 1984.
Dan Butnariu, Yair Censor, and Simeon Reich.
Iterative averaging of entropic projections for solving stochastic
convex feasibility problems.
Comput. Optim. Appl., 8(1):21-39, 1997.
Dan Butnariu, Alfredo N. Iusem, and Regina S. Burachik.
Iterative methods of solving stochastic convex feasibility problems
and applications.
Comput. Optim. Appl., 15(3):269-307, 2000.
Dan Butnariu, Alfredo N. Iusem, and Regina S. Burachik.
Iterative methods of solving stochastic convex feasibility problems
and applications.
Comput. Optim. Appl., 15(3):269-307, 2000.
Dan Butnariu and Elena Resmerita.
The outer Bregman projection method for stochastic feasibility
problems in Banach spaces.
In Inherently parallel algorithms in feasibility and
optimization and their applications (Haifa, 2000), volume 8 of Stud.
Comput. Math., pages 69-86. North-Holland, Amsterdam, 2001.
Richard H. Byrd, Thomas Derby, Elizabeth Eskow, Klaas P.B. Oldenkamp, and
Robert B. Schnabel.
A new stochastic/perturbation method for large-scale global
optimization and its application to water cluster problems.
In Large scale optimization (Gainesville, FL, 1993), pages
68-81. Kluwer Acad. Publ., Dordrecht, 1994.
R. Caballero, E. Cerdá, M. M. Muñoz, L. Rey, and I. M. Stancu-Minasian.
Efficient solution concepts and their relations in stochastic
multiobjective programming.
J. Optim. Theory Appl., 110(1):53-74, 2001.
R. Caballero, E. Cerda, M. M. Muñoz, and L. Rey.
Analysis and comparisons of some solution concepts for stochastic
programming problems.
Top, 10(1):101-123, 2002.
Rafael Caballero, Emilio Cerdá, María del Mar Muñoz, and Lourdes
Rey.
Stochastic approach versus multiobjective approach for obtaining
efficient solutions in stochastic multiobjective programming problems.
European J. Oper. Res., 158(3):633-648, 2004.
Yong Xin Cai, Bao Liu, and Ji Song Kou.
A novel method for solving chance constrained stochastic programming
problems.
J. Tianjin Univ., 4:50-57, 1985.
M. Cain and M.L.R. Price.
Optimal mixture choice.
J. R. Stat. Soc., Ser. C 35, 1-7, 1986.
R. Cairoli and Robert C. Dalang.
Sequential stochastic optimization.
Wiley Series in Probability and Statistics: Probability and
Statistics. John Wiley & Sons Inc., New York, 1996.
A Wiley-Interscience Publication.
G. C. Calafiore and L. El Ghaoui.
On distributionally robust chance-constrained linear programs.
J. Optim. Theory Appl., 130(1):1-22, 2006.
Giuseppe Calafiore and Boris T. Polyak.
Stochastic algorithms for exact and approximate feasibility of robust
LMIs.
IEEE Trans. Automat. Control, 46(11):1755-1759, 2001.
J.R. Callahan and C.R. Bector.
Optimization with general stochastic objective functions.
In Proceedings of the Third Manitoba Conference on Numerical
Mathematics (Winnipeg, Man., 1973), pages 127-137, Winnipeg, Man., 1974.
Utilitas Math.
J.R. Callahan and C.R. Bector.
Optimization with general stochastic objective functions.
Z. Angew. Math. Mech., 55(9):528-530, 1975.
J. Calvin and A. Zilinskas.
On the convergence of the P-algorithm for one-dimensional global
optimization of smooth functions.
J. Optim. Theory Appl., 102(3):479-495, 1999.
J. Calvin and A. Zilinskas.
One-dimensional P-algorithm with convergence rate O(n\sp -3+d) for smooth functions.
J. Optim. Theory Appl., 106(2):297-307, 2000.
J. Calvin and A. Zilinskas.
One-dimensional P-algorithm with convergence rate O(n\sp -3+d) for smooth functions.
J. Optim. Theory Appl., 106(2):297-307, 2000.
J. M. Calvin and A. Zilinskas.
One-dimensional global optimization for observations with noise.
Comput. Math. Appl., 50(1-2):157-169, 2005.
James M. Calvin.
Non-adaptive coverings for optimization of Gaussian random fields.
In Monte Carlo and quasi-Monte Carlo methods in scientific
computing (Las Vegas, NV, 1994), volume 106 of Lecture Notes in
Statist., pages 149-157. Springer, New York, 1995.
James M. Calvin.
Average performance of a class of adaptive algorithms for global
optimization.
Ann. Appl. Probab., 7(3):711-730, 1997.
James M. Calvin.
Nonadaptive univariate optimization for observations with noise.
In Models and algorithms for global optimization, volume 4 of
Springer Optim. Appl., pages 185-192. Springer, New York, 2007.
James M. Calvin and Antanas Zilinskas.
On the choice of statistical model for one-dimensional
P-algorithms.
Control Cybernet., 29(2):555-565, 2000.
James M. Calvin and Antanas Zilinskas.
On convergence of a P-algorithm based on a statistical model of
continuously differentiable functions.
J. Global Optim., 19(3):229-245, 2001.
International Workshop on Global Optimization, Part 2 (Firenze,
1999).
David Canning.
Average behavior in learning models.
J. Econom. Theory, 57(2):442-472, 1992.
X. R. Cao.
Single sample path-based optimization of Markov chains.
J. Optim. Theory Appl., 100(3):527-548, 1999.
Xi-Ren Cao.
Convergence of parameter sensitivity estimates in a stochastic
experiment.
IEEE Trans. Autom. Control AC-30, 845-853, 1985.
Andrew Caplin and John Leahy.
The recursive approach to time inconsistency.
J. Econom. Theory, 131(1):134-156, 2006.
Robert Carbone.
Public facilities location under stochastic demand.
INFOR-Canad. J. Operational Res. and Information Processing,
12:261-270, 1974.
David R. Cariño, David H. Myers, and William T. Ziemba.
Concepts, technical issues, and uses of the Russell-Yasuda
Kasai financial planning model.
Oper. Res., 46(4):450-462, 1998.
David R. Cariño and William T. Ziemba.
Formulation of the Russell-Yasuda Kasai financial planning
model.
Oper. Res., 46(4):433-449, 1998.
D.R. Cariño, T. Kent, D.H. Myers, C. Stacy, M. Sylvanus, A.L. Turner,
K. Watanabe, and W.T. Ziemba.
The Russel-Yasuda-Kasai model: An asset-liability model for a
Japanese insurance company using multistage stochastic programming.
Interfaces, 24(1):29-49, 1994.
C.C. Carøe, A. Ruszczy\'nski, and R. Schultz.
Unit commitment under uncertainty via two-stage stochastic
programming.
Technical Report DIKU-TR-97/23, Department of Computer Science,
University of Copenhagen, 1997.
C.C. Carøe and J. Tind.
A cutting-plane approach to mixed 0-1 stochastic integer programs.
European Journal of Operational Research, 101(2):306-316,
1997.
Claus C. Carøe.
Decomposition in stochastic integer programming.
Ph.d. thesis, University of Copenhagen, Denmark, 1998.
Claus C. Carøe and Rüdiger Schultz.
Dual decomposition in stochastic integer programming.
Oper. Res. Lett., 24(1-2):37-45, 1999.
Claus C. Carøe and Jørgen Tind.
L-shaped decomposition of two-stage stochastic programs with integer
recourse.
Math. Programming, 83(3, Ser. A):451-464, 1998.
Richard J. Caron, Arnon Boneh, and Shahar Boneh.
Redundancy.
In Advances in sensitivity analysis and parametric programming,
pages 13.1-13.41. Kluwer Acad. Publ., Boston, MA, 1997.
R.J. Caron, M. Hlynka, and J.F. McDonald.
On the best case performance of hit and run methods for detecting
necessary constraints.
Math. Programming, 54(2, Ser. A):233-249, 1992.
P. Carpentier, G. Cohen, and J.-C. Culioli.
Stochastic optimal control and decomposition-coordination methods -
part i: Theory.
In: Recent Developments in Optimization, Roland Durier and
Christian Michelot (Eds.), LNEMS 429:72-87, Springer-Verlag, Berlin, 1995.
P. Carpentier, G. Cohen, and J.-C. Culioli.
Stochastic optimal control and decomposition-coordination methods -
part ii: Application.
In: Recent Developments in Optimization, Roland Durier and
Christian Michelot (Eds.), LNEMS 429:88-103, Springer-Verlag, Berlin, 1995.
P. Carpentier, G. Cohen, J.-C. Culioli, and A. Renaud.
Stochastic optimization of unit commitment: a new decomposition
framework.
IEEE Transactions on Power Systems 11(2):1067-1073, 1996.
Robert L. Carraway, Thomas L. Morin, and Herbert Moskowitz.
Generalized dynamic programming for stochastic combinatorial
optimization.
Oper. Res., 37(5):819-829, 1989.
Robert L. Carraway, Robert L. Schmidt, and Lawrence R. Weatherford.
An algorithm for maximizing target achievement in the stochastic
knapsack problem with normal returns.
Naval Res. Logist., 40(2):161-173, 1993.
E. Carrizosa, M. Muñoz-Márquez, and J. Puerto.
Optimal positioning of a mobile service unit on a line.
Ann. Oper. Res., 111:75-88, 2002.
Recent developments in the theory and applications of location
models, Part II.
Gh. Cartianu and Edmond Nicolau, editors.
Prelucrarea si transmiterea numerica a datelor si
conducerea proceselor cu ajutorul calculatoarelor, volume 10 of
Probleme de Automatizare [Problems of Automation].
Editura Academiei Republicii Socialiste România, Bucharest, 1978.
D. Carton.
Programmes linéaires stochastiques.
Bull. Direction Études Recherches Sér. C Math. Informat.,
1(3):43-60, 1968.
A. Casado and R. Blanco.
A method for transforming stochastic nonlinear programming problems
into deterministic ones for a class T of functions.
Investigación Oper., 7(2):3-9, 1986.
A. Casado and J. Ruíz.
On a stochastic quadratic programming problem.
Investigación Oper., 5(1):3-11, 1984.
Michael S. Casey and Suvrajeet Sen.
The scenario generation algorithm for multistage stochastic linear
programming.
Math. Oper. Res., 30(3):615-631, 2005.
R.G. Cassidy, C.A. Field, and M.J.L. Kirby.
Two stage programming under uncertainty: a game theoretic approach.
Cahiers Centre Etud. Rech. oper. 15, 39-55, 1973.
David A. Castañon and Z. Bo Tang.
A counterexample to: "Adaptive partitioned random search to global
optimization" [IEEE Trans. Automat. Control 39 (1994),
no. 11, 2235-2244; MR 95m:90123] by Tang.
IEEE Trans. Automat. Control, 41(2):310, 1996.
J. Casti.
Forest monitoring and harvesting policies.
Appl. Math. Comput. 12, 19-48, 1983.
Olivier Catoni.
The energy transformation method for the Metropolis algorithm
compared with simulated annealing.
Probab. Theory Related Fields, 110(1):69-89, 1998.
Douglas W. Caves and Joseph A. Herriges.
Optimal dispatch of interruptible and curtailable service options.
Oper. Res. 40, No.1, 104-112, 1992.
Yair Censor, Alvaro R. De Pierro, and Alfredo N. Iusem.
Optimization of Burg's entropy over linear constraints.
Appl. Numer. Math., 7(2):151-165, 1991.
Yair Censor and Arnold Lent.
Optimization of "log x" entropy over linear equality
constraints.
SIAM J. Control Optim., 25(4):921-933, 1987.
Yair Censor and Stavros A. Zenios.
Parallel Optimization: Theory, Algorithms and Applications.
Oxford University Press, Oxford, UK, 1997.
I.P. Cernopiskii.
Remarks on the existence of a saddle point in stochastic programming
problems.
In Mathematical methods for the study and optimization of
systems (Proc. Sem. Theory of Optimal Decisions, Kiev, 1970) (Russian),
pages 221-224, Kiev., 1971. Akad. Nauk Ukrain. SSR Inst. Kibernet.
Cristiano Cervellera, Aihong Wen, and Victoria C. P. Chen.
Neural network and regression spline value function approximations
for stochastic dynamic programming.
Comput. Oper. Res., 34(1):70-90, 2007.
Debjani Chakraborty.
Redefining chance-constrained programming in fuzzy environment.
Fuzzy Sets and Systems, 125(3):327-333, 2002.
Theme: decision and optimization.
T.K. Chakraborty.
Stochastic parameter single sampling plans of given strength.
Bull., Calcutta Stat. Assoc. 41, No.161-164, 117-126, 1991.
T.K. Chakraborty.
Single sampling LTPD plans as a stochastic programming
problem.
Sankhy¯a Ser. B, 54(2):242-248, 1992.
Raymond H. Chan and Wai Ki Ching.
A direct method for stochastic automata networks.
In Applied probability (Hong Kong, 1999), volume 26 of
AMS/IP Stud. Adv. Math., pages 1-15. Amer. Math. Soc., Providence, RI,
2002.
T. M. Chan.
Geometric applications of a randomized optimization technique.
Discrete Comput. Geom., 22(4):547-567, 1999.
14th Annual ACM Symposium on Computational Geometry (Minneapolis, MN,
1998).
Suresh Chandra and T.R. Gulati.
A duality theorem for a nondifferentiable fractional programming
problem.
Management Sci., 23(1):32-37, 1975/76.
C.B. Chang and L.C. Youens.
Measurement correlation for multiple sensor tracking in a dense
target environment.
IEEE Trans. Autom. Control AC-27, 1250-1252, 1982.
H. S. Chang.
On the probability of correct selection by distributed voting in the
stochastic optimization.
J. Optim. Theory Appl., 125(1):231-240, 2005.
Hyeong Soo Chang.
Multi-policy improvement in stochastic optimization with forward
recursive function criteria.
J. Math. Anal. Appl., 305(1):130-139, 2005.
Hyeong Soo Chang, Michael C. Fu, Jiaqiao Hu, and Steven I. Marcus.
An asymptotically efficient simulation-based algorithm for finite
horizon stochastic dynamic programming.
IEEE Trans. Automat. Control, 52(1):89-94, 2007.
Ryong Sop Chang.
An algorithm for stochastic extremum problems with upper-half
gradient.
Cho-son In-min Kong-hwa-kuk Kwa-hak-won T 'ong-bo,
5:10-15, 1983.
Ryong Sop Chang.
An algorithm of the modified stochastic conjugate gradient method and
its convergence.
Cho-son In-min Kong-hwa-kuk Kwa-hak-won T 'ong-bo,
2:18-22, 1984.
Shih Sen Chang and Nan Jing Huang.
Generalized random multivalued quasi-complementarity problems.
Indian J. Math., 35(3):305-320, 1993.
Pavel Charamza.
Comparison of the stochastic approximation software.
In Computational aspects of model choice (Prague, 1991),
Contrib. Statist., pages 163-176. Physica, Heidelberg, 1993.
Moses Charikar, Chandra Chekuri, and Martin Pál.
Sampling bounds for stochastic optimization.
In Approximation, randomization and combinatorial optimization,
volume 3624 of Lecture Notes in Comput. Sci., pages 257-269. Springer,
Berlin, 2005.
V. Charles and D. Dutta.
A method for solving linear stochastic fractional programming problem
with mixed constraints.
Acta Cienc. Indica Math., 30(3):497-506, 2004.
V. Charles and D. Dutta.
Linear stochastic fractional programming with
sum-of-probabilistic-fractional objective.
Optimization Online, http://www.optimization-online.org, 2005.
V. Charles and D. Dutta.
Non-linear stochastic fractional programming models of financial
derivatives.
Optimization Online, http://www.optimization-online.org, 2005.
A. Charnes, Y.C. Chang, and J. Semple.
Semi-infinite relaxation of joint chance constraints in
chance-constrained programming. I. Zero-order stochastic decision rules.
Internat. J. Systems Sci., 23(7):1051-1061, 1992.
A. Charnes and W.W. Cooper.
A comment on Blau's dilemma in stochastic programming and
Bayesian decision analysis.
Management Sci., 22(4):498-500, 1975/76.
A. Charnes, W.W. Cooper, and M.J.L. Kirby.
Chance-constrained programming: an extension of statistical method.
In Optimizing methods in statistics (Proc. Sympos., Ohio State
Univ., Columbus, Ohio, 1971), pages 391-402, New York, 1971. Academic
Press.
A. Charnes, W.W. Cooper, D. Klingman, and R.J. Niehaus.
Explicit solutions in convex goal programming.
Management Sci. 2 438-448 (1976)., 1975.
A. Charnes, W.W. Cooper, and G.H. Symonds.
Cost horizons and certainty equivalents: An approach to stochastic
programming of heating oil.
Management Science, 4:183-195, 1958.
A. Charnes, Fred Glover, and D. Klingman.
The lower bounded and partial upper bounded distribution model.
Naval. Res. Logist. Quart. 18, 277-281, 1971.
A. Charnes and M. Kirby.
Optimal decision rules for the triangular E-model of
chance-constrained programming.
Cah. Cent. Etud. Rech. Oper. 11, 215-243, 1969.
A. Charnes, M.J.L. Kirby, and W.M. Raike.
An acceptance region theory for chance-constrained programming.
J. Math. Anal. Appl. 32, 38-61, 1970.
Abraham Charnes, Kingsley E. Haynes, Jared E. Hazleton, and Michael J. Ryan.
An hierarchical goal programming approach to environmental-land use
management.
In Math. Anal. Decis. Probl. Ecol., Proc. NATO Conf., Istanbul
1973, Lect. Notes Biomath. 5, 2-13, 1975.
Bernard Chazelle.
Deterministic sampling and optimization.
In System modelling and optimization (Compiègne, 1993),
volume 197 of Lecture Notes in Control and Inform. Sci., pages 42-54.
Springer, London, 1994.
Baoqian Chen.
The convergence of a random search algorithm for unconstrained
optimization problems.
Math. Numer. Sin. 6, 166-173, 1984.
Han-Fu Chen.
Optimization based on information containing uncertainties.
Kybernetes, 30(9-10):1177-1182, 2001.
Han-Fu Chen.
Stochastic approximation and its applications, volume 64 of
Nonconvex Optimization and its Applications.
Kluwer Academic Publishers, Dordrecht, 2002.
Han-Fu Chen and Hai-Tao Fang.
Nonconvex stochastic optimization for model reduction.
J. Global Optim., 23(3-4):359-372, 2002.
Nonconvex optimization in control.
Hanfu Chen.
Stochastic approximation algorithm randomly truncated at
time-varying bounds.
Annu. Rev. Autom. Program. 12, part 1, 380-383, 1985.
Hanfu Chen.
Stochastic approximation with state-dependent noise.
Sci. China Ser. E, 43(5):531-541, 2000.
Hanfu Chen and Lei Guo.
Stochastic adaptive pole-zero assignment with convergence analysis.
Syst. Control Lett. 7, 159-164, 1986.
Hanfu Chen and Qian Wang.
Continuous-time Kiefer-Wolfowitz algorithm with randomized
differences.
Systems Sci. Math. Sci., 11(4):327-341, 1998.
H.F. Chen and P.E. Caines.
The strong consistency of the stochastic gradient algorithm of
adaptive control.
IEEE Trans. Autom. Control AC-30, 189-192, 1985.
H.F. Chen, T.E. Duncan, and B. Pasik-Duncan.
A stochastic approximation algorithm with random differences.
In Proceedings of the 13th IFAC World Congress, volume H,
pages 493-496, 1996.
Hong Chen, Ping Yang, David D. Yao, and Xiuli Chao.
Optimal control of a simple assembly system.
Oper. Res. Lett., 14(4):199-205, 1993.
Michael Chen and Sanjay Mehrotra.
Self-concordant tree and decomposition based interior point methods
for stochastic convex optimization problem.
Stochastic Programming E-Print Series, http://www.speps.org,
2007.
Patrick S. Chen.
On the minimum solution of the inventory system for deteriorating
items with shortages and time-varying demand.
J. Inform. Optim. Sci., 22(2):283-295, 2001.
Shao Zhong Chen and Zuo Shu Liu.
Random fixed-point theorems in abstract spaces.
Acta Math. Sci., 1(2):133-144, 1981.
Shenghui Chen and Qinghua Chen.
A weighted network model based on the preferential selection of
edges.
Appl. Math. Sci. (Hikari), 1(21-24):1145-1156, 2007.
Shih-Pin Chen.
An alternating variable method with varying replications for
simulation response optimization.
Comput. Math. Appl., 48(5-6):769-778, 2004.
Victoria C. P. Chen.
Measuring the goodness of orthogonal array discretizations for
stochastic programming and stochastic dynamic programming.
SIAM J. Optim., 12(2):322-344 (electronic), 2001/02.
X. Chen.
Newton-type methods for stochastic programming.
Math. Comput. Modelling, 31(10-12):89-98, 2000.
Stochastic models in engineering, technology, and management (Gold
Coast, 1996).
Xiao Jun Chen, Li Qun Qi, and Robert S. Womersley.
Newton's method for quadratic stochastic programs with recourse.
J. Comput. Appl. Math., 60(1-2):29-46, 1995.
Linear/nonlinear iterative methods and verification of solution
(Matsuyama, 1993).
Xiaojun Chen and Robert S. Womersley.
A parallel inexact Newton method for stochastic programs with
recourse.
Ann. Oper. Res., 64:113-141, 1996.
Stochastic programming, algorithms and models (Lillehammer, 1994).
Z. L. Chen and W. B. Powell.
Convergent cutting-plane and partial-sampling algorithm for
multistage stochastic linear programs with recourse.
J. Optim. Theory Appl., 102(3):497-524, 1999.
Zhi-Long Chen, Shanling Li, and Devanath Tirupati.
A scenario-based stochastic programming approach for technology and
capacity planning.
Comput. Oper. Res., 29(7):781-806, 2002.
Zhi Ping Chen and Yong Gao.
Stationary and Markov properties of optimal solution processes for
stochastic programming problems with random processes.
Pure Appl. Math., 12(1):88-92, 1996.
Zhi Ping Chen and Yong Gao.
Process properties and stability of optimal solution sets of
stochastic programming problems with random processes.
Acta Math. Appl. Sinica, 20(3):466-472, 1997.
Zhi Ping Chen and Cheng Xian Xu.
Generalized duality theory for general multistage programming
problems with recourse.
J. Math. Res. Exposition, 17(2):275-286, 1997.
Zhiping Chen.
On the convergence of sampling algorithms for solving dynamic
stochastic programming.
Systems Sci. Math. Sci., 13(4):397-406, 2000.
Zhiping Chen and Chengxian Xu.
A dual gradient method for solving a class of two-stage compensating
problems.
Math. Appl., 9(3):266-271, 1996.
Zhiping Chen and Chengxian Xu.
Global convergence of a general sampling algorithm for dynamic
nonlinear stochastic programs.
Numer. Funct. Anal. Optim., 23(5-6):495-514, 2002.
T.C.E. Cheng.
Capacity requirements planning by stochastic linear programming.
Math. Modelling 7, 443-448, 1986.
A.G. Chentsov.
On the correct extension of some problems with stochastic
constraints.
Avtomat. i Telemekh., 7:68-79, 1995.
Myun-Seok Cheon, Shabbir Ahmed, and Faiz Al-Khayyal.
A branch-reduce-cut algorithm for the global optimization of
probabilistically constrained linear programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Myun-Seok Cheon, Shabbir Ahmed, and Faiz Al-Khayyal.
A branch-reduced-cut algorithm for the global optimization of
probabilistically constrained linear programs.
Math. Program., 108(2-3, Ser. B):617-634, 2006.
Raymond K.-M. Cheung and Warren B. Powell.
SHAPE-a stochastic hybrid approximation procedure for
two-stage stochastic programs.
Oper. Res., 48(1):73-79, 2000.
Raymond K.-M. Cheung and Warren B. Powell.
SHAPE-a stochastic hybrid approximation procedure for two-stage
stochastic programs.
Oper. Res., 48(1):73-79, 2000.
Tran Quoc Chien.
Semistochastic decomposition scheme in mathematical programming and
game theory.
Kybernetika (Prague), 27(6):535-541, 1991.
A. Chikan and I. Dobos.
A concept of uncertainty and risk.
PU.M.A., Pure Math. Appl., Ser. C 2, No.1, 87-94, 1991.
J.B. Chilton.
Maximum principle for the optimal control of systems with continuous
leads.
J. Optim. Theory Appl., 69(2):343-350, 1991.
D.C. Chin.
A more efficient global optimization algorithm based on Styblinski
and Tang.
Neural Networks, 7:573-574, 1994.
D.C. Chin.
Efficient identification procedure for inversion processing.
In Proceedings of the IEEE Conference on Decision and
Control, pages 3129-3130, 1996.
D.C. Chin.
Comparative study of stochastic algorithms for system optimization
based on gradient approximations.
IEEE Transactions on Systems, Man, and Cybernetics B,
27:244-249, 1997.
D.C. Chin.
The simultaneous perturbation method for processing magnetospheric
images.
Optical Engineering, 38:606-611, 1999.
D.C. Chin and R.H. Smith.
A traffic simulation for mid-Manhattan with model-free adaptive
signal control.
In Proceedings of the Summer Computer Simulation Conference,
pages 296-301, 1994.
D.C. Chin and R. Srinivasan.
Electrical conductivity object locator: location of small objects
buried at shallow depths.
In Proceedings of the Unexploded Ordnance (UXO) Conference,
pages 50-57, 1997.
Anukal Chiralaksanakul and David P. Morton.
Assessing policy quality in multi-stage stochastic programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Gyeong-Mi Cho.
Stability analysis of two-stage stochastic programming problems.
Bull. Korean Math. Soc., 31(2):243-252, 1994.
Gyeong-Mi Cho.
Stability of the multiple objective linear stochastic programming
problems.
Bull. Korean Math. Soc., 32(2):287-296, 1995.
Gyeong-Mi Cho.
Stability in two-stage multiobjective stochastic programming.
Nonlinear Anal., 47(6):3641-3648, 2001.
Proceedings of the Third World Congress of Nonlinear Analysts, Part 6
(Catania, 2000).
Michal Chobot.
A stochastic approach to goal programming.
Ekonom.-Mat. Obzor, 9:305-319, 1973.
Milan Chobot.
Goal-interval programming, and a stochastic approach to
decentralization problems.
In Second National Conference on Mathematical Methods in
Economics (Harmónia, 1972) (Czech), pages 69-93. Ekonom.-Mat. Lab.
Ekonom. Ústavu Ceskoslov. Akad. Ved, Prague, 1973.
Christine Choirat, Christian Hess, and Raffaello Seri.
Approximation of stochastic programming problems.
In Monte Carlo and quasi-Monte Carlo methods 2004, pages
45-59. Springer, Berlin, 2006.
Tahir Choulli, Michael Taksar, and Xun Yu Zhou.
Excess-of-loss reinsurance for a company with debt liability and
constraints on risk reduction.
Quant. Finance, 1(6):573-596, 2001.
Tahir Choulli, Michael Taksar, and Xun Yu Zhou.
Excess-of-loss reinsurance for a company with debt liability and
constraints on risk reduction.
Quant. Finance, 1(6):573-596, 2001.
Chee-Seng Chow and John N. Tsitsiklis.
The complexity of dynamic programming.
J. Complexity 5, No.4, 466-488, 1989.
Yunshyong Chow and June Hsieh.
On occupation times of annealing processes.
Bull. Inst. Math. Acad. Sinica, 20(1):19-26, 1992.
Anthony H. Christer, Shunji Osaki, and Lyn C. Thomas, editors.
Stochastic modelling in innovative manufacturing, volume 445 of
Lecture Notes in Economics and Mathematical Systems.
Springer-Verlag, Berlin, 1997.
Papers from the UK-Japanese Workshop held at Churchill College,
University of Cambridge, Cambridge, July 21-22, 1995.
D. Christiansen and S.W. Wallace.
Option theory and modeling under uncertainty.
In E. Matson, A. Tomasgard, E. Meistad and S. Dye (eds.), Essays
in honour of Bjorn Nygreen on his 50th Birthday, Dept. of Man. Econ. and OR,
Norwegian Univ. of Sc. and Technology, Trondheim, 147-168, 1996.
H.Dalgas Christiansen.
Monte Carlo optimization in K-space. - Global and path approach.
In Proc. Stat. Comput. Sect., Annu. Meet. Am. Stat. Assoc.,
Cincinnati/Ohio 1982, 200-205, 1982.
Piotr Chrzan.
An application of sample distributions for seeking the öptimal"
solution for probabilistic programming problems.
Przegl. Stat. 28, 29-38, 1981.
Piotr Chrzan.
Application of the Chebyshev inequality for determining
deterministic equivalents of probabilistic models.
Przeglk ad Statyst., 28(3-4):247-254 (1982), 1981.
S.N. Chuang, T.T. Soong, and K.H. Chen.
Stochastic extensions to necessary conditions in the theory of the
calculus of variations.
J. Optimization Theory Appl., 23(1):53-64, 1977.
Chia-Shin Chung and James Flynn.
Optimal replacement policies for k-out-of-n systems.
IEEE Trans. Reliab. R-38, No.4, 462-467, 1989.
S.L. Chung, F.B. Hanson, and H.H. Xu.
Parallel stochastic dynamic programming: finite element methods.
Linear Algebra Appl., 172:197-218, 1992.
Second NIU Conference on Linear Algebra, Numerical Linear Algebra and
Applications (DeKalb, IL, 1991).
Olga R. Chuyan and Aleksei G. Sukharev.
On adaptive and nonadaptive stochastic and deterministic algorithms.
J. Complexity, 6(1):119-127, 1990.
T. Cipra.
Gaussian processes in linear programs with random right-hand sides.
Z. Angew. Math. Mech., 68(5):T438-T439, 1988.
Tomás Cipra.
Moment problem with given covariance structure in stochastic
programming.
Ekonom.-Mat. Obzor, 21(1):66-77, 1985.
Tomás Cipra.
Prediction in stochastic linear programming.
Kybernetika (Prague), 23(3):214-226, 1987.
Tomás Cipra.
Autoregressive processes in optimization.
J. Appl. Probab., 25(2):302-312, 1988.
Tomás Cipra.
Stochastic programming with random processes.
Ann. Oper. Res., 30(1-4):95-105, 1991.
Stochastic programming, Part I (Ann Arbor, MI, 1989).
Tomás Cipra and Jitka Dupacová.
Selected applications of stochastic programming and a survey of
software.
Ekonom.-Mat. Obzor, 22(3):241-262, 1986.
A.M. Cirlin.
Optimierung im Mittel und gleitende Regimes in Problemen der
optimalen Regelung.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1974, Nr. 2, 143-151,
1974.
Steven P. Clark and Peter C. Kiessler.
On the convexity of value functions for a certain class of stochastic
dynamic programming problem.
Stochastic Anal. Appl., 20(4):783-789, 2002.
Hans Joachim Cleef.
Loesungsverfahren fuer eine Klasse zweistufiger stochastischer
Optimierungsprobleme.
Mathematisch-Naturwissenschaftliche Fakultaet der Rheinischen
Friedrich- Wilhelms-Universitaet zu Bonn. 80 S., 1980.
H.J. Cleef.
A solution procedure for the two-stage stochastic program with simple
recourse.
Z. Oper. Res. Ser. A-B, 25(1):A1-A13, 1981.
H.J. Cleef and W. Gaul.
A stochastic flow problem.
J. Inf. Optimization Sci. 1, 229-270, 1980.
H.J. Cleef and W. Gaul.
Project scheduling via stochastic programming.
Math. Operationsforsch. Statist. Ser. Optim., 13(3):449-468,
1982.
Moise Cocan.
Ueber die Bellmansche Funktionalgleichung fuer stochastische
dynamische Prozesse. (On the Bellman functional equation for stochastic
dynamic processes).
Bul. Univ. Brasov, Ser. C 31, 13-18, 1989.
Moise Cocan.
The model of a stochastic dynamic programming problem in case of
processes with an infinite number of periods.
Bul. Univ. Bra sov Ser. C, 32:55-61, 1990.
Ju.M. Codikov and A.S. Hohlov.
Extrapolation fuer innere Penaltyfunktionen.
Izv. Akad. Nauk SSSR, Tekh. Kibernet. 1977, No.2, 32-38, 1977.
E. G. Coffman, Jr., George S. Lueker, Joel Spencer, and Peter M. Winkler.
Packing random rectangles.
Probab. Theory Related Fields, 120(4):585-599, 2001.
Jr. Coffman, E.G., D.S. Johnson, P.W. Shor, and G.S. Lueker.
Probabilistic analysis of packing and related partitioning problems.
In Probability and algorithms, pages 87-107. Nat. Acad. Press,
Washington, DC, 1992.
G. Cohen.
Information exchange between independent stochastic systems.
Journal of Optimization Theory and Applications 32(2), 1980.
G. Cohen and P. Bernhard.
On the rationality of some decision rules in a stochastic
environment.
IEEE Transactions on Automatic Control, AC-24, Nr. 5, 1979.
G. Cohen and J.-C. Culioli.
Decomposition and coordination in stochastic optimization.
4th IFAC Symposium on Large Scale Systems Theory and
Applications, Zurich, 1986.
Guy Cohen and Jean-Christophe Culioli.
Algorithmes de décomposition/coordination en optimisation
stochastique.
RAIRO Automat.-Prod. Inform. Ind., 20(3):253-272, 1986.
Ch. Condevaux-Lanloy, E. Fragniere, and A.J. King.
SISP, simplified interface for stochastic programming establishing
a hard link between mathematical programming modeling languages and SMPS
codes.
Optimization Methods and Software, 17(3):423-443, 2002.
Christian Condevaux-Lanloy, Emmanuel Fragnière, and Alan J. King.
SISP: simplified interface for stochastic programming: establishing
a hard link between mathematical programming modeling languages and SMPS
codes.
Optim. Methods Softw., 17(3):423-443, 2002.
Stochastic programming.
E. Contini.
A stochastic approach to goal programming.
Oper. Res. 16, 576-586, 1968.
R. Cooke and J.D. Pinter.
Optimization in risk management.
Civil Engineering Systems, 6:122-128, 1989.
Leon Cooper.
The stochastic transportation-location problem.
Comput. Math. Appl., 4(3):265-275, 1978.
William W. Cooper, H. Deng, Zhimin Huang, and Susan X. Li.
Chance constrained programming approaches to congestion in stochastic
data envelopment analysis.
European J. Oper. Res., 155(2):487-501, 2004.
W.W. Cooper, Zhimin Huang, and Susan X. Li.
Satisficing DEA models under chance constraints.
Ann. Oper. Res., 66:279-295, 1996.
Extensions and new developments in data envelopment analysis.
Don Coppersmith and Gregory B. Sorkin.
Constructive bounds and exact expectations for the random assignment
problem.
Random Structures Algorithms, 15(2):113-144, 1999.
A. Corana, M. Marchesi, C. Martini, and S. Ridella.
Minimizing multimodal functions of continuous variables with the
"simulated annealing" algorithm.
ACM Trans. Math. Software 13, 262-280, 1987.
Adrian Corban.
The stochastic threedimensional transportation problem.
Stud. Cercet. Mat. 24, 513-517, 1972.
Adrian Corban.
Linear tridimensional programming.
Revue Roumaine Math. pur. appl. 20, 897-905, 1975.
K. Cormican, D.P. Morton, and R.K. Wood.
Stochastic network interdiction.
Operations Research, 46:184-197, 1998.
Andrea Corradini, Ugo Montanari, Francesca Rossi, Hartmut Ehrig, and Michael
Loewe.
Graph grammars and logic programming.
In Graph grammars and their application to computer science,
Proc. 4th Int. Workshop, Bremen/Ger. 1990, Lect. Notes Comput. Sci. 532,
221-237, 1991.
Andre Costa.
Ants, stochastic optimisation and reinforcement learning.
Austral. Math. Soc. Gaz., 32(2):116-123, 2005.
A.M. Couhault.
Quelques methodes de resolution d'un probleme de programmation
stochastique lineaire venant de la gestion des stocks.
IRIA, Cahier Nr. 9, 77-100, 1972.
A.B. Cox and Jr. Bryson, A.E.
Identification by a combined smoothing nonlinear programming
algorithm.
Automatica-J. IFAC, 16(6):689-694, 1980.
Dennis D. Cox and Susan John.
SDO: a statistical method for global optimization.
In Multidisciplinary design optimization (Hampton, VA, 1995),
pages 315-329. SIAM, Philadelphia, PA, 1997.
Louis Anthony Cox, Jr. and Djangir A. Babayev.
Optimization under uncertainty via random sampling of scenarios. I.
Appl. Comput. Math., 3(2):95-106, 2004.
M. L. A. G. Cremers, W. K. Klein Haneveld, and M. H. van der Vlerk.
A two-stage model for a day-ahead paratransit planning problem.
In CTW2006-Cologne-Twente Workshop on Graphs and Combinatorial
Optimization, volume 25 of Electron. Notes Discrete Math., page 35
(electronic). Elsevier, Amsterdam, 2006.
Allan B. Cruse.
Some combinatorial properties of centrosymmetric matrices.
Linear Algebra and Appl., 16(1):65-77, 1977.
Janos Csirik.
Bin packing as a random walk: A note on Knoedel's paper.
Oper. Res. Lett. 5, 161-163, 1986.
M. Cugiani.
Some stochastic strategies in optimization problems.
In Variational inequalities and complementarity problems,
theory and applications, Proc. int. School Math., Erice/Sicily 1978,
105-126, 1980.
Di Cui, Xiang Bin Sun, and Wei Zhang.
Stochastic programming with an improved Wolef-BFGS-SQP method.
J. Shandong Univ. Sci. Technol. Nat. Sci., 24(2):94-96, 2005.
J.-C. Culioli and G. Cohen.
Decomposition/coordination algorithms in stochastic optimization.
SIAM J. Control Optim., 28(6):1372-1403, 1990.
J.-C. Culioli and G. Cohen.
Optimisation stochastique sous contraintes en espérance.
Comptes Rendus de l'Académie des Sciences, Paris, t.320,
Série I, 735-758, 1995.
A.A. Cunningham and D.M. Frances.
A data collection strategy for estimation of cost coefficients of a
linear programming model.
Management Sci. 22, 1074-1080, 1976.
V.I. Curkov.
The two-stage stochastic programming problem of block type.
Z. Vycisl. Mat. i Mat. Fiz., 18(2):360-369, 524, 1978.
Jaksa Cvitani\'c and Ioannis Karatzas.
Convex duality in constrained portfolio optimization.
Ann. Appl. Probab., 2(4):767-818, 1992.
Ja.Z. Cypkin, A.I. Kaplinskii, and A.S. Krasnenker.
Methoden der lokalen Verbesserung in Problemen der stochastischen
Optimierung.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1973, Nr. 6, 3-11, 1973.
N.D. Dagkesamanskii.
A stochastic problem of optimization of multimode installations.
In Questions of mathematical cybernetics and applied
mathematics, No. 3 (Russian), pages 93-101. "Èlm", Baku, 1978.
Roy W. Dahl, Karen Keating, Laurence Levy, Daryl J. Salamone, Barindra Nag, and
Joan A. Sanborn.
Weighted Hamiltonian augmentation method involving insertions.
J. Guid. Control Dyn. 11, No.5, 412-414, 1988.
L. Dai, C. H. Chen, and J. R. Birge.
Convergence properties of two-stage stochastic programming.
J. Optim. Theory Appl., 106(3):489-509, 2000.
Frédéric Dambreville and Jean-Pierre Le Cadre.
Detection of a Markovian target with optimization of the search
efforts under generalized linear constraints.
Naval Res. Logist., 49(2):117-142, 2002.
A.R. Danilin.
Regularization of a control problem under conditions of
uncertainty.
In Some questions of operator theory, Collect. sci. Works,
Sverdlovsk 1987, 20-28, 1987.
G. Dantzig and G. Infanger.
Approaches to stochastic programming with application to electric
power systems.
In K. Frauendorfer, H. Glavitsch, and R. Bacher, editors,
Optimization in Planning and Operation of Electric Power Systems, pages
125-138. Physica-Verlag, Heidelberg, 1993.
G. B. Dantzig.
Decomposition techniques for large-scale electric power systems
planning under uncertainty.
In R. Sharda, B. L. Golden, E. Wasil, O. Balci, and W. Steward,
editors, Impacts of Recent Computer Advances on Operations Research,
pages 3-20. North-Holland, 1989.
G. B. Dantzig and G. Infanger.
Intelligent control and optimization under uncertainty with
application to hydro power.
European Journal of Operational Research, 97(2):396-407, 1997.
G.B. Dantzig.
Linear programming under uncertainty.
Management Science, 1:197-206, 1955.
G.B. Dantzig, M.A.H. Dempster, and M. Kallio, editors.
Large Scale Linear Programming, Vols. 1 and 2.
IIASA, Laxenburg, 1981.
George B. Dantzig.
A generalized programming solution to a convex programming problem
with a homogeneous objective.
Research Report RR-73-21, IIASA - International Institute for Applied
Systems Analysis, Laxenburg, Austria, 1973.
George B. Dantzig.
Need to do planning under uncertainty and the possibility of using
parallel processors for this purpose.
Ekonom.-Mat. Obzor, 23(2):121-135, 1987.
George B. Dantzig and Peter W. Glynn.
Parallel processors for planning under uncertainty.
Ann. Oper. Res., 22(1-4):1-21, 1990.
Supercomputers and large-scale optimization: algorithms, software,
applications (Minneapolis, MN, 1988).
George B. Dantzig and Gerd Infanger.
Large-scale stochastic linear programs-importance sampling and
Benders decomposition.
In Computational and applied mathematics, I (Dublin, 1991),
pages 111-120. North-Holland, Amsterdam, 1992.
George B. Dantzig and Gerd Infanger.
Multi-stage stochastic linear programs for portfolio optimization.
Ann. Oper. Res., 45(1-4):59-76, 1993.
B. S. Darkhovskii.
On an approach to the stochastic recovery problem.
Avtomat. i Telemekh., (9):43-52, 2000.
M.S. Daskin.
A maximum expected covering location model: formulation, properties
and heuristic solution.
Transportation Science, 17(1):48-70, 1983.
Gianfranco D'Atri.
Outline of a probabilistic framework for combinatorial optimization.
In Numerical techniques for stochastic systems (Conf., Gargnano,
1979), pages 347-368. North-Holland, Amsterdam, 1980.
Gianfranco D'Atri and Claude Puech.
Probabilistic analysis of the subset-sum problem.
Discrete Appl. Math. 4, 329-334, 1982.
K.J. Daun, J.R. Howell, and D.P. Morton.
Geometric optimization of radiative enclosures containing specular
surfaces, through non-linear programming.
In International Mechanical Engineering Congress and
Exposition, 2002.
K.J. Daun, J.R. Howell, and D.P. Morton.
Geometric optimization of radiative enclosures containing specular
surfaces.
Journal of Heat Transfer, 125:845-851, 2003.
K.J. Daun, J.R. Howell, and D.P. Morton.
Smoothing monte carlo exchange factors through constrained maximum
likelihood estimation.
Journal of Heat Transfer, 127:1124-1128, 2005.
H.T. David and Geung-Ho Kim.
Pragmatic optimization of information functionals.
In Optimizing methods in statistics, Proc. int. Conf., Bombay
1977, 167-181 , 1979.
M. Davidson.
Primal-dual constraint aggregation with application to stochastic
programming.
Ann. Oper Res., 99:41-58 (2001), 2000.
Applied mathematical programming and modeling, IV (Limassol, 1998).
M. R. Davidson and N. M. Novikova.
The constraint aggregation method in a game-theoretic problem of
stochastic programming.
Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet.,
48(2):26-29, 1998.
M. R. Davidson, N. M. Novikova, and D. D. Solomakhin.
A method for searching for a stochastic saddle point with constraints
satisfied almost surely.
Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet.,
(2):10-14, 48, 2000.
M. R. Davidson and D. D. Solomakhin.
A method for solving a stochastic programming problem with
constraints satisfied almost surely.
Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet.,
50(2):33-36, 1999.
M.H.A. Davis.
Markov models and optimization, volume 49 of Monographs on
Statistics and Applied Probability.
Chapman & Hall, London, 1993.
M.H.A. Davis, M.A.H. Dempster, and R.J. Elliott.
On the value of information in controlled diffusion processes.
In Stochastic analysis, pages 125-138. Academic Press, Boston,
MA, 1991.
G.V. Davydov and I.M. Davydova.
Solubility of the system Ax=0, x ³ 0 with indefinite
coefficients.
Sov. Math. 34, No.9, 108-112 translation from Izv. Vyssh.
Uchebn. Zaved., Mat. 1990, No.9(340), 85-88 (1990)., 1990.
R.van Dawen and M. Schael.
On the existence of stationary optimal policies in Markov decision
models.
Z. Angew. Math. Mech. 63, T403-T404, 1983.
P.K. De, D. Acharya, and K.C. Sahu.
A chance-constrained goal programming model for capital budgeting.
J. Oper. Res. Soc. 33, 635-638, 1982.
Prabuddha De, Jay B. Ghosh, and Charles E. Wells.
On the minimization of the weighted number of tardy jobs with random
processing times and deadline.
Comput. Oper. Res. 18, No.5, 457-463, 1991.
Prabuddha De, Jay B. Ghosh, and Charles E. Wells.
On the solution of a stochastic bottleneck assignment problem and
its variations.
Nav. Res. Logist. 39, No.3, 389-397, 1992.
V. de Angelis.
Minimax solution for linear programming problems under conditions of
uncertainty.
Metron, 34(1-2):101-121 (1978), 1976.
V. de Angelis.
Linear programming with uncertain objective function: minimax
solution for relative loss.
Calcolo, 16(2):125-141, 1979.
V. de Angelis.
Stochastic linear programming in the objective function: minimax
solution for relative loss.
In Proceedings of the First World Conference on Mathematics at
the Service of Man (Barcelona, 1977), Vol. I, pages 248-271. Univ. Politec.
Barcelona, 1980.
X. de Groote, M.-C. Noël, and Y. Smeers.
Some test problems for stochastic nonlinear multistage programs.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 515-541. Springer, Berlin, 1988.
W.J. De Lange and J.D. Pinter.
Groundwater quality assessment and management: A stochastic modelling
approach.
Research Report 91.009, National Institute for Inland Water
Management and Waste Water Treatment, Lelystad, 1991.
F. De Vylder.
Bound on integrals: elimination of the dual and reduction of the
number of equality constraints.
Insurance Math. Econom., 2(3):139-145, 1983.
F. De Vylder.
Maximization, under equality constraints, of a functional of a
probability distribution.
Insurance Math. Econom., 2(1):1-16, 1983.
I. Deák.
Computation of multiple normal probabilities.
In Recent results in stochastic programming (Proc. Meeting,
Oberwolfach, 1979), volume 179 of Lecture Notes in Econom. and Math.
Systems, pages 107-120, Berlin, 1980. Springer.
I. Deák.
Three digit accurate multiple normal probabilities.
Numer. Math., 35(4):369-380, 1980.
I. Deák.
Computing probabilities of rectangles in case of multinormal
distribution.
J. Statist. Comput. Simulation, 26(1-2):101-114, 1986.
I. Deák.
Multidimensional integration and stochastic programming.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 187-200. Springer, Berlin, 1988.
István Deák.
Monte Carlo methods for computing probabilities of sets in
higher-dimensional spaces in case of normal distribution.
Alkalmaz. Mat. Lapok, 4(1-2):35-94 (1979), 1978.
István Deák.
Regression estimators related to multinormal distributions: computer
experiences in root finding.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 279-293. Springer, Berlin, 1998.
István Deák.
Computing two-stage stochastic programming problems by successive
regression approximations.
In Stochastic optimization techniques (Neubiberg/Munich, 2000),
volume 513 of Lecture Notes in Econom. and Math. Systems, pages
91-102. Springer, Berlin, 2002.
István Deák.
Solving stochastic programming problems by successive regression
approximations-numerical results.
In Dynamic stochastic optimization (Laxenburg, 2002), volume
532 of Lecture Notes in Econom. and Math. Systems, pages 209-224.
Springer, Berlin, 2004.
István Deák.
Two-stage stochastic problems with correlated normal variables:
computational experiences.
Ann. Oper. Res., 142:79-97, 2006.
Brian C. Dean, Michel X. Goemans, and Jan Vondrák.
Adaptivity and approximation for stochastic packing problems.
In Proceedings of the Sixteenth Annual ACM-SIAM Symposium on
Discrete Algorithms, pages 395-404 (electronic), New York, 2005. ACM.
Morris H. DeGroot.
Optimization with an uncertain objective.
Stochastics, 21(2):97-112, 1987.
I. Dekany.
Numerical solution of a stochastic control problem derived from
Bensoussan-Lions. Inventory model.
In Prog. Oper. Res., Eger 1974, Colloq. Math. Soc. Janos Bolyai
12, 231-242 , 1976.
P. Del Moral and G. Salut.
Particle interpretation of nonlinear filtering and optimization.
Russian J. Math. Phys., 5(3):355-372 (1998), 1997.
Pierre Del Moral.
Maslov optimization theory: topological aspects.
In Idempotency (Bristol, 1994), pages 354-382. Cambridge Univ.
Press, Cambridge, 1998.
Bernard Delyon and Anatoli Juditsky.
Stochastic optimization with averaging of trajectories.
Stochastics Stochastics Rep., 39(2-3):107-118, 1992.
Ron S. Dembo.
Scenario optimization.
Ann. Oper. Res., 30(1-4):63-80, 1991.
Stochastic programming, Part I (Ann Arbor, MI, 1989).
Ron S. Dembo and Moshe Haviv.
Truncated policy iteration methods.
Oper. Res. Lett. 3, 243-246, 1984.
R.S. Dembo and A. King.
Tracking models and the optimal regret distribution in asset
allocation.
Applied Stochastic Models and Data Analysis 8:151-157, 1992.
M. A. H. Dempster.
Sequential importance sampling algorithms for dynamic stochastic
programming.
Zap. Nauchn. Sem. S.-Peterburg. Otdel. Mat. Inst. Steklov.
(POMI), 312(Teor. Predst. Din. Sist. Komb. i Algoritm. Metody. 11):94-129,
312-313, 2004.
M. A. H. Dempster, J. E. Scott, and G. W. P. Thompson.
Stochastic modeling and optimization using STOCHASTICS.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 137-157. SIAM, Philadelphia, PA, 2005.
M.A.H. Dempster.
On stochastic programming. I: Static linear programming under risk.
J. math. Analysis Appl. 21, 304-343, 1968.
M.A.H. Dempster, editor.
Stochastic programming, London, 1980. Academic Press Inc.
[Harcourt Brace Jovanovich Publishers].
The Institute of Mathematics and its Applications Conference Series.
M.A.H. Dempster.
A stochastic approach to hierarchical planning and scheduling.
In Deterministic and stochastic scheduling (Durham, 1981),
volume 84 of NATO Adv. Study Inst. Ser. C: Math. Phys. Sci., pages
271-296, Dordrecht, 1982. Reidel.
M.A.H. Dempster, editor.
Proceedings of the IIASA Task Force Meeting in Stochastic
Optimization.
1983.
Stochastics 10, No. 3-4.
M.A.H. Dempster.
On stochastic programming. II. Dynamic problems under risk.
Stochastics, 25(1):15-42, 1988.
M.A.H. Dempster, M.L. Fisher, L. Jansen, B.J. Lageweg, J.K. Lenstra, and A.H.G.
Rinnooy Kan.
Analytical evaluation of hierarchical planning systems.
Oper. Res. 29, 707-716, 1981.
M.A.H. Dempster, M.L. Fisher, L. Jansen, B.J. Lageweg, J.K. Lenstra, and A.H.G.
Rinnooy Kan.
Analysis of heuristics for stochastic programming: results for
hierarchical scheduling problems.
Math. Oper. Res., 8(4):525-537, 1983.
M.A.H. Dempster and A. Papagaki-Papoulias.
Computational experience with an approximate method for the
distribution problem.
In Stochastic programming, Proc. int. Conf., Oxford 1974,
223-243, 1980.
Eric V. Denardo, Uriel G. Rothblum, and Arthur J. Swersey.
A transportation problem in which costs depend on the order of
arrival.
Manage. Sci. 34, No.6, 774-783, 1988.
D. Dentcheva.
Differentiable selections and Castaing representations of
multifunctions.
Mathematical Analysis and Applications, 223:371-396, 1998.
D. Dentcheva.
Regular Castaing representations with application to stochastic
programming.
SIAM Journal on Optimization, 10:732-749, 2000.
D. Dentcheva, A. Prekopa, and A. Ruszczynski.
On convex probabilistic programs with discrete distributions.
Nonlinear Analysis: Theory, Methods & Applications,
47:1997-2009, 2001.
D. Dentcheva, A. Prekopa, and A. Ruszczynski.
Bounds for integer stochastic programs with probabilistic
constraints.
Discrete Applied Mathematics, to appear.
D Dentcheva and W. Römisch.
Optimal power generation under uncertainty via stochastic
programming.
In K. Marti and P. Kall, editors, Stochastic Programming Methods
and Technical Applications, Lecture Notes in Economics and Mathematical
Systems, volume 458, pages 22-56. Springer-Verlag, Berlin, 1998.
D. Dentcheva and A. Ruszczy\'nski.
Inverse stochastic dominance constraints and quantile utility theory.
C. R. Acad. Bulgare Sci., 58(1):13-18, 2005.
Darinka Dentcheva.
Regular Castaing representations of multifunctions with
applications to stochastic programming.
SIAM J. Optim., 10(3):732-749 (electronic), 2000.
Darinka Dentcheva.
Approximations, expansions and univalued representations of
multifunctions.
Nonlinear Anal., 45(1, Ser. A: Theory Methods):85-108, 2001.
Darinka Dentcheva, René Henrion, and Andrzej Ruszczy\'nski.
Stability and sensitivity of optimization problems with first order
stochastic dominance constraints.
SIAM J. Optim., 18(1):322-337 (electronic), 2007.
Darinka Dentcheva, Bogumila Lai, and Andrzej Ruszczy\'nski.
Efficient point methods for probabilistic optimization problems.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Darinka Dentcheva, Bogumila Lai, and Andrzej Ruszczy\'nski.
Dual methods for probabilistic optimization problems.
Math. Methods Oper. Res., 60(2):331-346, 2004.
Darinka Dentcheva, András Prékopa, and Andrzej Ruszczy\'nski.
Concavity and efficient points of discrete distributions in
probabilistic programming.
Math. Program., 89(1, Ser. A):55-77, 2000.
Darinka Dentcheva, András Prékopa, and Andrzej Ruszczy\'nski.
On convex probabilistic programming with discrete distributions.
Nonlinear Anal., 47(3):1997-2009, 2001.
Proceedings of the Third World Congress of Nonlinear Analysts, Part 3
(Catania, 2000).
Darinka Dentcheva, András Prékopa, and Andrzej Ruszczy\'nski.
Bounds for probabilistic integer programming problems.
Discrete Appl. Math., 124(1-3):55-65, 2002.
Workshop on Discrete Optimization (Piscataway, NJ, 1999).
Darinka Dentcheva and Werner Römisch.
Differential stability of two-stage stochastic programs.
SIAM J. Optim., 11(1):87-112 (electronic), 2000.
Darinka Dentcheva and Werner Römisch.
Duality gaps in nonconvex stochastic optimization.
Math. Program., 101(3, Ser. A):515-535, 2004.
Darinka Dentcheva, Werner Römisch, and Rüdiger Schultz.
Strong convexity and directional derivatives of marginal values in
two-stage stochastic programming.
In Stochastic programming (Neubiberg/München, 1993), volume
423 of Lecture Notes in Econom. and Math. Systems, pages 8-21.
Springer, Berlin, 1995.
Darinka Dentcheva and Andrzej Ruszczy\'nski.
Optimality and duality theory for stochastic optimization problems
with nonlinear dominance constraints.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Darinka Dentcheva and Andrzej Ruszczy\'nski.
Optimization with stochastic dominance constraints.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Darinka Dentcheva and Andrzej Ruszczy\'nski.
Portfolio optimization with stochastic dominance constraints.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Darinka Dentcheva and Andrzej Ruszczy\'nski.
Convexification of stochastic ordering.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Darinka Dentcheva and Andrzej Ruszczy\'nski.
Optimality and duality theory for stochastic optimization problems
with nonlinear dominance constraints.
Math. Program., 99(2, Ser. A):329-350, 2004.
Darinka Dentcheva and Andrzej Ruszczy\'nski.
Semi-infinite probabilistic optimization: first-order stochastic
dominance constraints.
Optimization, 53(5-6):583-601, 2004.
Michel Denuit and Claude Lefèvre.
Stochastic s-(increasing) convexity.
In Generalized convexity and generalized monotonicity
(Karlovassi, 1999), pages 167-182. Springer, Berlin, 2001.
Michel Denuit, Claude Lefèvre, and Sergey Utev.
Stochastic orderings of convex/concave-type on an arbitrary grid.
Math. Oper. Res., 24(4):835-846, 1999.
Concetta A. DePaolo and David J. Rader, Jr.
A heuristic algorithm for a chance constrained stochastic program.
European J. Oper. Res., 176(1):27-45, 2007.
Cyrus Derman, Gerald J. Lieberman, and Sheldon M. Ross.
A sequential stochastic assignment problem.
Management Sci., Theory 18, 349-355, 1972.
C.L. Dert.
Asset Liability Management for Pension Funds, A Multistage
Chance Constrained Programming Approach.
PhD thesis, Erasmus University, Rotterdam, The Netherlands, 1995.
Luc P. Devroye.
An expanding automaton for use in stochastic optimization.
J. Cybern. Inf. Sci. 82-94 (1978)., 1977.
Luc P. Devroye.
Progressive global random search of continuous functions.
Math. Programming, 15(3):330-342, 1978.
Luc P. Devroye.
Inequalities for the completion times of stochastic PERT networks.
Math. Oper. Res. 4, 441-447, 1979.
I.P. Devyaterikov and A.I. Koshlan'.
A method of solving constrained stochastic optimization problems.
Autom. Remote Control 49, No.5, 628-632 translation from Avtom.
Telemekh. 1988, No.5, 99-105 (1988)., 1988.
I.P. Devyaterikov and A.I. Koshlan.
Stochastic optimization methods with constraints.
Autom. Remote Control 49, No.4, 397-412 translation from Avtom.
Telemekh. 1988, No.4, 3-21 (1988)., 1988.
A.S. Dexter, J.N.W. Yu, and W.T. Ziemba.
Portfolio selection in a lognormal market when the investor has a
power utility function: Computational results.
In Stochastic programming, Proc. int. Conf., Oxford 1974,
507-523, 1980.
Ang Zhao Di.
A constrained iterative stochastic algorithm.
Acta Automat. Sinica, 11(3):251-257, 1985.
Nico Di Domenica, George Birbilis, Gautam Mitra, and Patrick Valente.
Stochastic programming and scenario generation within a simualtion
framework.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
A. Di Nola and S. Sessa.
A Boolean model of deciding in fuzzy environment.
RAIRO Automat., 14(4):363-374, 1980.
With comments by A. Dussauchoy.
Josep Díaz, Mathew D. Penrose, Jordi Petit, and María Serna.
Approximating layout problems on random geometric graphs.
J. Algorithms, 39(1):78-116, 2001.
Boyan Dimitrov and Jr. Green, David.
Stochastic optimization problems under incomplete information on
distribution functions.
Control Cybernet., 26(1):93-110, 1997.
W. Dinkelbach.
Entscheidungsmodelle.
W. de Gruyter, Berlin, 1982.
(in German).
J. Dippon and J. Renz.
Weighted means in stochastic approximation of minima.
SIAM Journal of Control and Optimization, 35:1811-1827,
1997.
Jürgen Dippon.
Globally convergent stochastic optimization with optimal asymptotic
distribution.
J. Appl. Probab., 35(2):395-406, 1998.
L.C.W. Dixon.
On on-line variable metric methods.
In Survey of mathematical programming, Proc. int. Symp., Vol.
3, Budapest 1976, 421-428, 1980.
L.C.W. Dixon and L. James.
On stochastic variable-metric methods.
In Analysis and optimisation of stochastic systems (Proc.
Internat. Conf., Univ. Oxford, Oxford, 1978), pages 243-256. Academic
Press, London, 1980.
D.T. Dochev and V.I. Zhukovski.
An s1-optimal solution of a multicriterial problem with
uncertainty.
Godishnik Vissh. Uchebn. Zaved. Prilozhna Mat., 23(2):9-26
(1988), 1987.
D.T. Dochev and V.I. Zhukovski.
Sufficient conditions for p1-optimality with uncertainty.
Godishnik Vissh. Uchebn. Zaved. Prilozhna Mat., 23(1):19-30
(1988), 1987.
Stefcho P. Dokov and David P. Morton.
Higher-order upper bounds on the expectation of a convex function.
Stochastic Programming E-Print Series, http://www.speps.org,
2002.
Steftcho P. Dokov and David P. Morton.
Second-order lower bounds on the expectation of a convex function.
Math. Oper. Res., 30(3):662-677, 2005.
V.A. Dolodarenko.
The statement of a complex problem of optimal control over a
dynamical system with combinatorial plan.
In Applied problems of the dynamics of controlled movement,
Collect. sci. Works, Kiev 1981, 119-127, 1981.
V.A. Dolodarenko and E.I. Fedan.
Ueber eine Methode zur Loesung des Zuordnungsproblems mit
nichtlinearer stochastischer Zielfunktion.
Kibernetika, Kiev 1977, Nr. 2, 136-141, 1977.
V.A. Dolodarenko and V.S. Sen'kin.
On an optimization problem under given probability of aim reaching.
In Probabilistic-statistical methods for investigations of
complex systems, Work Collect., Kiev 1976, 40-45, 1976.
V.O. Dolodarenko and E.I. Fedan.
An allocation problem with non-linear stochastic objective
function.
Avtomatika, Kiev 1976, Nr. 3, 29-32, 1976.
Christopher J. Donohue and John R. Birge.
An upper bound on the expected value of a non-increasing convex
function with convex marginal return functions.
Oper. Res. Lett., 18(5):213-221, 1996.
Christopher J. Donohue and John R. Birge.
The abridged nested decomposition method for multistage stochastic
linear programs with relatively complete recourse.
Algorithmic Oper. Res., 1(1):20-30, 2006.
C. C. Y. Dorea and L. C. Zhao.
Convergence of a random algorithm for function optimization.
Numer. Funct. Anal. Optim., 20(9-10):825-833, 1999.
C.C.Y. Dorea.
Stochastic algorithm for solving a set of inequalities.
Numer. Funct. Anal. Optim., 10(9-10):937-945, 1989.
C.C.Y. Dorea.
Stopping rules for a random optimization method.
SIAM J. Control Optimization 28, No.4, 841-850, 1990.
C.C.Y. Dorea and C.R. Gonçalves.
Alternative sampling strategy for a random optimization algorithm.
J. Optim. Theory Appl., 78(2):401-407, 1993.
Chang C. Y. Dorea and Cátia R. Gonçalves.
Search schemes for random optimization algorithms that preserve the
asymptotic distribution.
J. Appl. Probab., 36(3):825-836, 1999.
Chang Chung Yu Dorea.
Stationary distribution of Markov chains in r\sp d with
application to global random optimization.
Bernoulli, 3(4):415-427, 1997.
Chang C.Y. Dorea.
Limiting distribution for random optimization methods.
SIAM J. Control Optim., 24(1):76-82, 1986.
Chang C.Y. Dorea.
Effort associated with a class of random optimization methods.
Math. Programming, 50(1 (Ser. A)):91-98, 1991.
Jan van Doremalen.
Mean value analysis in multichain queueing networks: An iterative
approximation.
In Operations research, Proc. 12th Annu. Meet., Mannheim 1983,
441-448 , 1984.
A.A. Dorogovtsev.
Stochastic analysis and a minimization problem.
Ukraïn. Mat. Zh., 46(11):1568-1571, 1994.
P. Doschkinov.
A distribution stability result for a stochastic optimal control
problem.
Optimization 18, 419-431, 1987.
Julian Douglass, Owen Wu, and William T. Ziemba.
Stock ownership decisions in dc pension plans.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Mihai Dragomirescu.
An algorithm for the minimum-risk problem of stochastic
programming.
Operations Res. 20, 154-164, 1972.
S.J. Drijver, W.K. Klein Haneveld, and M.H. van der Vlerk.
Asset liability management modeling using multistage mixed-integer
stochastic programming.
Research Report 00E52, SOM, University of Groningen, 2000.
S.J. Drijver, W.K. Klein Haneveld, and M.H. van der Vlerk.
ALM model for pension funds: numerical results for a prototype
model.
Research Report 02A44, SOM, University of Groningen,
http://som.rug.nl, 2002.
S.J. Drijver, W.K. Klein Haneveld, and M.H. van der Vlerk.
Asset Liability Management modeling using multi-stage mixed-integer
Stochastic Programming.
In B. Scherer, editor, Asset and Liability Management Tools: A
Handbook for Best Practice, pages 309-324. Risk Books, London, 2003.
Moshe Dror.
Modeling vehicle routing with uncertain demands as a stochastic
program: Properties of the corresponding solution.
Eur. J. Oper. Res. 64, No.3, 432-441, 1993.
Moshe Dror, Gilbert Laporte, and Pierre Trudeau.
Vehicle routing with stochastic demands: properties and solution
frameworks.
Transportation Sci., 23(3):166-176, 1989.
Moshe Dror and Pierre Trudeau.
Stochastic vehicle routing with modified savings algorithm.
Eur. J. Oper. Res. 23, 228-235, 1986.
Didier Dubois.
Linear programming with fuzzy data.
In Analysis of fuzzy information, Vol. 3: Appl. eng. sci.,
241-263, 1987.
Yu.A. Dubov.
Necessary and sufficient conditions for Pareto-optimality in mean.
Eng. Cybern. 17, No.6, 109-114 translation from Izv. Akad. Nauk
SSSR, Tekh. Kibern. 1979, No.6, 137-141 (1979)., 1979.
Alan I. Duchan.
A clarification and a new proof of the certainty equivalence
theorem.
Internat. econom. Review 15, 216-224, 1974.
Ewa Dudek-Dyduch and Tadeusz Dyduch.
On optimization with random seeking.
In Transactions of the Tenth Prague Conference on Information
Theory, Statistical Decision Functions, Random Processes, Vol. A (Prague,
1986), pages 293-298. Reidel, Dordrecht, 1988.
Gunter Dueck and Tobias Scheuer.
Threshold accepting: A general purpose optimization algorithm
appearing superior to simulated annealing.
J. Comput. Phys. 90, No.1, 161-175, 1990.
W. Duerr.
Stochastische Programmierungsmodelle als Vektormaximumprobleme.
In Proc. Oper. Res., DGU Ann. Meet. 1971, 189-199, 1972.
J. H. Dulá, R. V. Helgason, and N. Venugopal.
An algorithm for identifying the frame of a pointed finite conical
hull.
INFORMS J. Comput., 10(3):323-330, 1998.
José H. Dulá.
An upper bound on the expectation of simplicial functions of
multivariate random variables.
Math. Programming, 55(1, Ser. A):69-80, 1992.
José H. Dulá.
Bounds and approximations in stochastic linear programming.
Investigación Oper., 14(2-3):127-147, 1993.
Workshop on Stochastic Optimization: the State of the Art (Havana,
1992).
José H. Dulá.
Designing a majorization scheme for the recourse function in
two-stage stochastic linear programming.
Comput. Optim. Appl., 1(4):399-414, 1993.
Jose H. Dula and Rajluxmi V. Murthy.
A Tchebysheff-type bound on the expectation of sublinear polyhedral
functions.
Oper. Res. 40, No.5, 914-922, 1992.
Vincentiu Dumitru and Florica Luban.
On some optimization problems under uncertainty.
Fuzzy Sets Syst. 18, 257-272, 1986.
J. Dupacová.
In Studies on mathematical programming (Papers, Third Conf.
Math. Programming, Mátrafüred, 1975), Budapest.
J. Dupacova.
Stochastic programming models in banking.
Tutorial Paper, IIASA Laxenburg, February 1991.
J. Dupacová.
Minimax approach to stochastic linear programming and the moment
problem. Selected results.
Z. Angew. Math. Mech., 58(7):T466-T467, 1978.
J. Dupacová.
Experience in stochastic programming models.
In Survey of mathematical programming (Proc. Ninth Internat.
Math. Programming Sympos., Budapest, 1976), Vol. 2, pages 99-105,
Amsterdam, 1979. North-Holland.
J. Dupacová.
Stability studies in stochastic programs with recourse. A special
case.
Z. Angew. Math. Mech., 62(5):T369-T370, 1982.
J. Dupacová.
Stability in stochastic programming with recourse.
Acta Univ. Carolin.-Math. Phys., 24(1):23-34, 1983.
J. Dupacová.
Stability in stochastic programming with recourse-estimated
parameters.
Math. Programming, 28(1):72-83, 1984.
J. Dupacová.
Stability in stochastic programming with recourse. Contaminated
distributions.
Math. Programming Stud., 27:133-144, 1986.
Stochastic programming 84. I.
J. Dupacová.
Stochastic Programming.
MS CSR, Prague, 1986.
(in Czech).
J. Dupacova.
Stochastic optimization models in banking.
EMO, 27:201-234, 1991.
In Czech.
J. Dupacova.
Applications of stochastic programming in finance.
In Atti del XVI convegno A.M.A.S.E.S., Treviso 1992, pages
13-30, 1992.
J. Dupacova.
Stochastic programming models in banking & portfolio optimization
under uncertainty.
In W. Runggaldier, editor, Stochastic Processes: Applications in
Mathematical Economics - Finance, Applied Mathematics Monographs 5, pages
19-20. C.N.R., 1992.
J. Dupacova.
Bounds for stochastic programs in particular for recourse problems.
Working paper WP-95-085, IIASA, Laxenburg, Austria, 1995.
J. Dupacova.
Scenario based stochastic programs: Resistance with respect to
sample.
Annals of Oper. Res., 64:21-38, 1996.
J. Dupacova.
Uncertainty about input data in portfolio management.
In M. Bertocchi et al., editor, Modelling Techniques for
Financial Markets and Bank Management, pages 17-33. Physica Verlag, 1996.
J. Dupacova.
Input analysis for a bond portfolio management model.
ZAMM, 77:541-542, 1997.
J. Dupacova.
Moment bounds for stochastic programs in particular for recourse
problems.
In V. Benes and J. Stepán, editors,
Distributions with Given Marginals and Moment Problems (Proc. of the 3rd
International Conference, Praha 1996), pages 199-204. Kluwer Acad. Publ,
1997.
J. Dupacova.
Stochastic programming: Minimax approach.
In C. A. Floudas & P. M. Pardalos, editor, Encyclopedia of
Optimization, volume 5, pages 327-330. Kluwer, 2001.
J. Dupacová.
Stress testing via contamination.
In Coping with uncertainty, volume 581 of Lecture Notes in
Econom. and Math. Systems, pages 29-46. Springer, Berlin, 2006.
J. Dupacova and M. Bertocchi.
From data to model and back to data: A bond portfolio management
problem.
EJOR, 134:261-278, 2001.
J. Dupacova, M. Bertocchi, and V. Moriggia.
Sensitivity analysis on inputs for a bond portfolio management model.
In P. Albrecht, editor, Aktuarielle Ansätze für
Finanz-Risiken AFIR 1996, Proc. of the VIth AFIR Colloquium, Nuremberg,
pages 783-793. VVW Karlsruhe, 1996.
See also Technical Report 16, Univ. of Bergamo, 1996.
J. Dupacova, M. Bertocchi, and V. Moriggia.
Postoptimality for a bond portfolio management model.
In C. Zopounidias, editor, New Operational Approaches in
Financial Modelling (Proc. of the 19th meeting of EURO WGFM, Chania, Crete,
1996), pages 49-62, Heidelberg, 1997. Physica Verlag.
See also Technical Report No. 13, Univ. of Bergamo 1997.
J. Dupacova, M. Bertocchi, and V. Moriggia.
Sensitivity analysis for a bond portfolio management model for the
italian market.
Cybernetics, 29:595-615, 2000.
J. Dupacova, M. Bertocchi, and V. Moriggia.
Sensitivity analysis of a bond portfolio management model for the
italian market.
Control & Dynamics, 29:595-615, 2000.
J. Dupacova, M. Bertocchi, and V. Moriggia.
Sensitivity of a bond portfolio's behavior with respect to random
movements in yield curve: A simulation study.
Ann. Oper. Res., 99:267-286, 2000.
J. Dupacova, M. Bertocchi, and V. Moriggia.
Postoptimality for scenario based financial models with an
application to bond portfolio management.
In W. Ziemba and J. Mulvey, editors, World Wide Asset and
Liability Management. Cambridge Univ. Press, To appear.
J. Dupacova, P. Charamza, and J. Mádl.
On stochastic aspects of a metal cutting problem.
In K. Marti and P. Kall, editors, Proc. of 2nd GAMM/IFIP
Workshop `Stochastic Optimization: Numerical Techniques and Engineering
Applications', LN in Economics and Math. Systems 423, pages 196-209.
Springer, 1995.
J. Dupacova, A. Gaivoronski, Z. Kos, and T. Szantai.
Stochastic programming in water management: A case study and a
comparison of solution techniques.
EJOR, 52:28-44, 1991.
J. Dupacová, J. Hurt, and J. Stepán.
Stochastic Modeling in Economics and Finance, volume 75 of
Applied Optimization.
Kluver Acad. Publ., 2002.
J. Dupacova, M. Bertocchi J. Abaffy, and V. Moriggia.
Generating scenarios for bond portfolios.
Bulletin of the Czech Econometric Society, 11:3-27, 2000.
J. Dupacova, J. Mádl, and P.Charamza.
A mathematical model for optimization of cutting conditions in
machining.
In U. Derigs, A. Bachem, and A. Drexl, editors, OR-proceedings
1994, pages 28-32. Springer, 1995.
J. Dupacová and K. Sladký.
Comparison of multistage stochastic programs with recourse and
stochastic dynamic programs with discrete time.
ZAMM Z. Angew. Math. Mech., 82(11-12):753-765, 2002.
4th GAMM-Workshop "Stochastic Models and Control Theory"
(Lutherstadt Wittenberg, 2001).
Jitka Dupacová.
Minimax stochastic programs with nonconvex nonseparable penalty
functions.
In Progress in operations research, Vols. I, II (Proc. Sixth
Hungarian Conf., Eger, 1974), pages 303-316. Colloq. Math. Soc. János
Bolyai, Vol. 12, Amsterdam, 1976. North-Holland.
Jitka Dupacová.
The minimax problem of stochastic linear programming and the moment
problem.
Ekonom.-Mat. Obzor, 13(3):279-307, 1977.
Jitka Dupacová.
Minimax stochastic programs with nonseparable penalties.
In Optimization techniques (Proc. Ninth IFIP Conf., Warsaw,
1979), Part 1, volume 22 of Lecture Notes in Control and Information
Sci., pages 157-163, Berlin, 1980. Springer.
Jitka Dupacová.
Water resources system modelling using stochastic programming with
recourse.
In Recent results in stochastic programming (Proc. Meeting,
Oberwolfach, 1979), volume 179 of Lecture Notes in Econom. and Math.
Systems, pages 121-133, Berlin, 1980. Springer.
Jitka Dupacová.
Stability in stochastic programming-probabilistic constraints.
In Stochastic optimization (Kiev, 1984), volume 81 of
Lecture Notes in Control and Inform. Sci., pages 314-325. Springer, Berlin,
1986.
Jitka Dupacová.
The minimax approach to stochastic programming and an illustrative
application.
Stochastics, 20(1):73-88, 1987.
Jitka Dupacová.
On some connections between parametric and stochastic programming.
In Parametric optimization and related topics (Plaue, 1985),
volume 35 of Math. Res., pages 74-81. Akademie-Verlag, Berlin, 1987.
Jitka Dupacová.
Stochastic programming with incomplete information: a survey of
results on postoptimization and sensitivity analysis.
Optimization, 18(4):507-532, 1987.
Jitka Dupacová.
On nonnormal asymptotic behavior of optimal solutions of stochastic
programming problems: the parametric case.
In Proceedings of the Fourth Prague Symposium on Asymptotic
Statistics (Prague, 1988), pages 205-214, Prague, 1989. Charles Univ.
Jitka Dupacová.
Stochastic programming-model building and selected applications.
Investigación Oper., 10(3):119-134, 1989.
Jitka Dupacová.
Stability and sensitivity analysis for stochastic programming.
Ann. Oper. Res., 27(1-4):115-142, 1990.
Jitka Dupacová.
On nonnormal asymptotic behavior of optimal solutions for stochastic
programming problems and on related problems of mathematical statistics.
Kybernetika (Prague), 27(1):38-52, 1991.
Jitka Dupacová.
On statistical sensitivity analysis in stochastic programming.
Ann. Oper. Res., 30(1-4):199-214, 1991.
Stochastic programming, Part I (Ann Arbor, MI, 1989).
Jitka Dupacová.
On interval estimates for optimal value of stochastic programs.
In P. Kall, editor, System modelling and optimization (Zurich,
1991), volume 180 of Lecture Notes in Control and Inform. Sci., pages
556-563. Springer, Berlin, 1992.
Jitka Dupacová.
Applications of stochastic programming under incomplete information.
J. Comput. Appl. Math., 56(1-2):113-125, 1994.
Stochastic programming: stability, numerical methods and applications
(Gosen, 1992).
Jitka Dupacová.
Multistage stochastic programs: the state-of-the-art and selected
bibliography.
Kybernetika (Prague), 31(2):151-174, 1995.
Jitka Dupacová.
Postoptimality for multistage stochastic linear programs.
Ann. Oper. Res., 56:65-78, 1995.
Stochastic programming (Udine, 1992).
Jitka Dupacová.
Stochastic programming: Approximation via scenarios.
In Proceedings of 3rd Caribbean Conference on Approximation and
Optimization. EMIS Master Server
http://www.emis.de/proceedings/index.html, 1995.
Jitka Dupacová.
Scenario-based stochastic programs: resistance with respect to
sample.
Ann. Oper. Res., 64:21-38, 1996.
Stochastic programming, algorithms and models (Lillehammer, 1994).
Jitka Dupacová.
Postoptimality analysis for scenario based stochastic programs: a
survey.
In J. Guddat et al., editor, Parametric optimization and related
topics, IV (Enschede, 1995), volume 9 of Approx. Optim., pages 43-57,
Frankfurt am Main, 1997. Lang.
Jitka Dupacová.
Stochastic programming: approximation via scenarios.
In Proceedings of the 3rd International Conference on
Approximation and Optimization in the Caribbean (Puebla, 1995), page 20 pp.
(electronic). Benemérita Univ. Autón. Puebla, Puebla, 1997.
Jitka Dupacová.
Reflections on robust optimization.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 111-127. Springer, Berlin, 1998.
Jitka Dupacová.
Portfolio optimization via stochastic programming: methods of output
analysis.
Math. Methods Oper. Res., 50(2):245-270, 1999.
Financial optimization.
Jitka Dupacová.
Stability properties of a bond portfolio management problem.
Ann. Oper Res., 99:251-265 (2001), 2000.
Applied mathematical programming and modeling, IV (Limassol, 1998).
Jitka Dupacová.
Output analysis for approximated stochastic programs.
In Stochastic optimization: algorithms and applications
(Gainesville, FL, 2000), volume 54 of Appl. Optim., pages 1-29.
Kluwer Acad. Publ., Dordrecht, 2001.
Jitka Dupacová.
Applications of stochastic programming: achievements and questions.
European J. Oper. Res., 140(2):281-290, 2002.
O.R. for a united Europe (Budapest, 2000).
Jitka Dupacová.
Reflections on output analysis for multistage stochastic linear
programs.
In Dynamic stochastic optimization (Laxenburg, 2002), volume
532 of Lecture Notes in Econom. and Math. Systems, pages 3-20.
Springer, Berlin, 2004.
Jitka Dupacová.
Uncertainties in stochastic programming models: The minimax
approach.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Jitka Dupacová and Marida Bertocchi.
Management of bond portfolios via stochastic
programming-postoptimality and sensitivity analysis.
In System modelling and optimization (Prague, 1995), pages
574-581. Chapman & Hall, London, 1996.
Jitka Dupacová, Giorgio Consigli, and Stein W. Wallace.
Scenarios for multistage stochastic programs.
Ann. Oper Res., 100:25-53 (2001), 2000.
Research in stochastic programming (Vancouver, BC, 1998).
Jitka Dupacová, Nicole Gröwe-Kuska, and Werner Römisch.
Scenario reduction in stochastic programming: An approach using
probability metrics.
Stochastic Programming E-Print Series, http://www.speps.org, to
appear in Math. Progr., 2000.
Jitka Dupacova and Zdenek Kos.
Chance-constrained and simulation models of water resources
systems.
Ekon.-Mat. Obz. 15, 178-191, 1979.
Jitka Dupacová and Jan Polivka.
Asset-liability management for czech pension funds using stochastic
programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Jitka Dupacová and Jan Polivka.
Stress testing for var an cvar.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Jitka Dupacová and Pavel Popela.
Melt control.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Jitka Dupacová and Pavel Popela.
Melt control: charge optimization via stochastic programming.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 277-297. SIAM, Philadelphia, PA, 2005.
Jitka Dupacová and Roger Wets.
Asymptotic behavior of statistical estimators and of optimal
solutions of stochastic optimization problems.
Ann. Statist., 16(4):1517-1549, 1988.
Paul Dupuis and Rahul Simha.
On sampling controlled stochastic approximation.
IEEE Trans. Autom. Control 36, No.8, 915-924, 1991.
G. Duru.
Planification de la production en avenir aleatoire.
Publ. econometr., Lyon 4, 381-411, 1971.
G. Duru.
Programme stochastique généralisé à un étage.
Publ. Économétriques, 5:17-36, 1972.
Prajit K. Dutta.
What do discounted optima converge to? A theory of discount rate
asymptotics in economic models.
J. Econom. Theory, 55(1):64-94, 1991.
Prajit K. Dutta and Mukul Majumdar.
Limit integration theorems for monotone functions and parametric
continuity in zero sum stochastic games.
Nonlinear World, 1(1):73-91, 1994.
Shane Dye.
Subtree decomposition for multistage stochastic programs.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Shane Dye, Leen Stougie, and Asgeir Tomasgard.
The stochastic single node service provision problem.
Stochastic Programming E-Print Series, http://www.speps.org,
2002.
M. Dyer and L. Stougie.
Stochastic programming problems: Complexity and approximability.
in preparation.
Martin Dyer and Alan Frieze.
Probabilistic analysis of the generalised assignment problem.
Math. Programming, 55(2, Ser. A):169-181, 1992.
Martin Dyer and Leen Stougie.
Computational complexity of stochastic programming problems.
Math. Program., 106(3, Ser. A):423-432, 2006.
M.E. Dyer, A.M. Frieze, and C.J.H. McDiarmid.
On linear programs with random costs.
Math. Program. 35, 3-16, 1986.
Richard L. Dykstra.
Computational aspects of I-projections.
J. Statist. Comput. Simulation, 21(3-4):265-274, 1985.
Richard L. Dykstra.
An iterative procedure for obtaining I-projections onto the
intersection of convex sets.
Ann. Probab., 13(3):975-984, 1985.
E.B. Dynkin.
Optimal programs and stimulating prices in the stochastic models of
economic growth.
In Math. Models Econ., Proc. Sympos. math. Meth. Econ. and
Conf. von Neumann Models, Warszawa 1972, 207-218, 1974.
E.B. Dynkin and A.A. Yushkevich.
Controllable Markov processes and their applications.
(Upravlyaemye markovskie protsessy i ikh prilozheniya).
Moskva: "Nauka", 1975.
R.G. Dyson.
Minimax solutions to stochastic programs - an aid to planning under
uncertainty.
J. Oper. Res. Soc. 29, 691-696, 1978.
Jr. Eberl, W. and O. Moeschlin.
On measurability in stochastic programming.
In Probability and statistical decision theory, Vol. A (Bad
Tatzmannsdorf, 1983), pages 97-105, Dordrecht, 1985. Reidel.
David Edelman.
On the financial value of information.
Ann. Oper Res., 100:123-132 (2001), 2000.
Research in stochastic programming (Vancouver, BC, 1998).
Chanaka Edirisinghe, Derek Atkins, and Paul Iyogun.
Bounds on stochastic convex allocation problems.
Appl. Stochastic Models Data Anal., 10(2):123-140, 1994.
N. C. P. Edirisinghe.
Bound-based approximations in multistage stochastic programming:
nonanticipativity aggregation.
Ann. Oper. Res., 85:103-127, 1999.
Stochastic programming. State of the art, 1998 (Vancouver, BC).
N. C. P. Edirisinghe, E. I. Patterson, and N. Saadouli.
Capacity planning model for a multipurpose water reservoir with
target-priority operation.
Ann. Oper. Res., 100:273-303 (2001), 2000.
Research in stochastic programming (Vancouver, BC, 1998).
N.C.P. Edirisinghe and G.-M. You.
Second-order scenario approximation and refinement in optimization
under uncertainty.
Ann. Oper. Res., 64:143-178, 1996.
Stochastic programming, algorithms and models (Lillehammer, 1994).
N.C.P. Edirisinghe and W.T. Ziemba.
Tight bounds for stochastic convex programs.
Oper. Res., 40(4):660-677, 1992.
N.C.P. Edirisinghe and W.T. Ziemba.
Bounding the expectation of a saddle function with application to
stochastic programming.
Math. Oper. Res., 19(2):314-340, 1994.
N.C.P. Edirisinghe and W.T. Ziemba.
Bounds for two-stage stochastic programs with fixed recourse.
Math. Oper. Res., 19(2):292-313, 1994.
N.C.P. Edirisinghe and W.T. Ziemba.
Implementing bounds-based approximations in convex-concave two-stage
stochastic programming.
Math. Programming, 75(2, Ser. B):295-325, 1996.
Approximation and computation in stochastic programming.
Ivan Edissonov.
The new ARSTI optimization method: adaptive random search
with translating intervals.
Amer. J. Math. Management Sci., 14(3-4):143-166, 1994.
J. Edwards.
A proposed standard input format for computer codes which solve
stochastic programs with recourse.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 215-227. Springer, Berlin, 1988.
V.M. Efimov.
A stochastic model of long term planning.
In Theory of optimal solutions (Proc. Sem., Kiev, 1969), No. 3
(Russian), pages 36-45, Kiev, 1969. Akad. Nauk Ukrain. SSR.
Pavlos S. Efraimidis and Paul G. Spirakis.
Combinatorial randomized rounding: boosting randomized rounding with
combinatorial arguments.
In Stochastic optimization: algorithms and applications
(Gainesville, FL, 2000), pages 31-53. Kluwer Acad. Publ., Dordrecht, 2001.
V. A. Efremov.
A numerical algorithm for solving a bilinear problem of quantile
optimization.
Jan Ehrhoff, Sven Grothklags, and Ulf Lorenz.
Disruption Management and Planning with Uncertainties in Aircraft
Planning.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Andreas Eichhorn and Werner Römisch.
Polyhedral risk measures in stochastic programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Andreas Eichhorn and Werner Römisch.
Polyhedral risk measures in stochastic programming.
SIAM J. Optim., 16(1):69-95 (electronic), 2005.
Andreas Eichhorn and Werner Römisch.
Stochastic integer programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Andreas Eichhorn and Werner Römisch.
Stability of multistage stochastic programs incorporating polyhedral
risk measures.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
Andreas Eichhorn and Werner Römisch.
Stochastic integer programming: limit theorems and confidence
intervals.
Math. Oper. Res., 32(1):118-135, 2007.
Andreas Eichhorn, Werner Römisch, and Isabel Wegner.
Polyhedral Risk Measures and Lagrangian Relaxation in Electricity
Portfolio Optimization.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Mark J. Eisner, Robert S. Kaplan, and John V. Soden.
Admissible decision rules for the E-model of chance-constrained
programming.
Management Sci., Theory 17, 337-353, 1971.
Mark J. Eisner and Paul Olsen.
Duality for stochastic programming interpreted as L. P. in Lp
space.
SIAM J. appl. Math. 28, 779-792, 1975.
Mark J. Eisner and Paul Olsen.
Duality in probabilistic programming.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 147-158, London, 1980. Academic Press.
Abou-Zaid H. El-Banna and Ebrahim A. Youness.
On stability of stochastic multiobjective programming problems with
random coefficients in the objective functions.
Matematiche (Catania), 49(1):3-9 (1995), 1994.
M.C. El Bouamri.
Minimisation en cascade des programmes convexes à temps discret et
à contrainte convexe non-anticipative.
Travaux Sém. Anal. Convexe, 12(2):exp. no. 14, 23, 1982.
M. El-Sayed Wahed.
Neural network representation of stochastic quadratic programming
problems when b\sb i's and c\sb j's follow goint distribution.
J. Inst. Math. Comput. Sci. Comput. Sci. Ser., 15(2):263-276,
2004.
D.F. Elliott and D.D. Sworder.
A variable metric technique for parameter optimization.
Automatica-J. IFAC, 5:811-816, 1969.
R.S. Ellis and R.W. Rishel.
An application of stochastic optimal control theory to the optimal
rescheduling of airplanes.
IEEE Trans. Automatic Control, AC-19:139-142, 1974.
Paul M. Ellner and Robert M. Stark.
On the distribution of the optimal value for a class of stochastic
geometric programs.
Naval Res. Logist. Quart., 27(4):549-571, 1980.
E.A. Elsayed and Mohammed Ettouney.
Perturbation analysis of linear programming problems with random
parameters.
Comput. Oper. Res. 21, No.2, 211-224, 1994.
K. H. Elster and R. Elster.
Optimization of c-orthogonal posynomials.
Acta Math. Vietnam., 22(1):71-105, 1997.
V.A. Emelicev and A.M. Kononenko.
The number of plans for a multi-index selection problem.
Dokl. Akad. Nauk BSSR, 18:677-680, 763, 1974.
A.K. Enaleev and D.A. Novikov.
Optimal stimulation mechanisms in an active system with probabilistic
uncertainty. I.
Avtomat. i Telemekh., 9:117-126, 1995.
Sebastian Engell, Andreas Märkert, Guido Sand, Rüdiger Schultz, and
Christian Schulz.
Online scheduling of multiproduct batch plants under uncertainty.
In Online optimization of large scale systems, pages 649-676.
Springer, Berlin, 2001.
Heinz W. Engl.
Existence of measurable optima in stochastic nonlinear programming
and control.
Appl. Math. Optimization 5, 271-281, 1979.
Katherine Bennett Ensor and Peter W. Glynn.
Stochastic optimization via grid search.
In Mathematics of stochastic manufacturing systems
(Williamsburg, VA, 1996), volume 33 of Lectures in Appl. Math., pages
89-100, Providence, RI, 1997. Amer. Math. Soc.
Robert Entriken.
Language constructs for modeling stochastic linear programs.
Ann. Oper Res., 104:49-66 (2002), 2001.
Modeling languages and systems.
Leah Epstein and Asaf Levin.
Tracking mobile users.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Leah Epstein and Rob van Stee.
Online scheduling of splittable tasks.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
E. Erdogan and G. Iyengar.
Ambiguous chance constrained problems and robust optimization.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
E. Erdogan and G. Iyengar.
Ambiguous chance constrained problems and robust optimization.
Math. Program., 107(1-2, Ser. B):37-61, 2006.
E. Erdogan and G. Iyengar.
On two-stage convex chance constrained problems.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
I.I. Eremin and Vl.D. Mazurov.
Nonstationary processes for mathematical programming problems under
the conditions of poorly formalized constraints and incomplete defining
information.
In Optim. Techn., IFIP techn. Conf. Novosibirsk 1974, Lect.
Notes Comput. Sci. 27, 37-41, 1975.
I.I. Erëmin and A.A. Vatolin.
Duality in improper mathematical programming problems under
uncertainty.
In Stochastic optimization (Kiev, 1984), volume 81 of
Lecture Notes in Control and Inform. Sci., pages 326-333. Springer, Berlin,
1986.
Horst Erfurth and Claus Bendzulla.
Die numerische Loesung eines speziellen Systems 1. Ordnung mit
verteilten Parametern. (Numerical solution of a special first-order system
with distributed parameters).
Wiss. Z. Tech. Hochschule Chemie Carl Schorlemmer
Leuna-Merseburg 13, 364- 367, 1971.
S.M. Ermakov and A.A. Zhiglyavskij.
On a random search of a global extremum.
Teor. Veroyatn. Primen. 28, No.1, 129-134, 1983.
S.M. Ermakov and A.A. Zhiglyavskij.
On random search for a global extremum.
Theory Probab. Appl. 28, 136-141, 1984.
S.M. Ermakov, A.A. Zhiglyavskij, and V.N. Solntsev.
On a random search scheme for the extremum of functions.
In Monte Carlo methods in numerical mathematics and
mathematical physics, Pap. VI. All-Union Conf., April 1979, Part 1,
Novosibirsk 1979, 17-24 , 1979.
O.V. Ermolenko.
The solution of two-stage stochastic problems with nonconvex
functions.
Kibernetika (Kiev), 3:100-102, 1976.
Ju. M. Ermol'ev.
Conditions for optimality in stochastic programming problems.
In Theory of optimal solutions (Proc. Sem., Kiev, 1969), No. 1
(Russian), pages 36-44, Kiev, 1969. Akad. Nauk Ukrain. SSR.
Ju. M. Ermol'ev.
The method of generalized stochastic gradients and stochastic
quasi-Fejér sequences.
Kibernetika (Kiev), 2:73-83, 1969.
Ju. M. Ermol'ev.
A certain general problem of stochastic programming.
Kibernetika (Kiev), 3:47-50, 1971.
Ju. M. Ermol'ev.
The convergence of random quasi-Féjer sequences.
Kibernetika (Kiev), 4:70-71, 1971.
Ju. M. Ermol'ev.
A method of generalized stochastic gradients, and its applications.
In Proceedings of the Fourth All-Union Conference on Automatic
Control-Engineering Cybernetics (Tbilisi, 1968), Vol. 1: Optimal and
adaptive systems (Russian), pages 230-236, 310. Izdat. "Nauka", Moscow,
1972.
Ju. M. Ermol'ev.
Stochastic models, and optimization methods.
Kibernetika (Kiev), 4:109-119, 1975.
Ju. M. Ermol'ev and A.M. Gupal.
An analogue of the linearization method in problems of the
minimization of nondifferentiable functions.
Kibernetika (Kiev), 1:65-68, 1978.
Ju. M. Ermol'ev and A.I. Jastremskii.
Stokhasticheskie modeli i metody v èkonomicheskom
planirovanii.
"Nauka", Moscow, 1979.
Èkonomiko-Matematicheskaya Biblioteka. [Mathematical Economics
Library].
Ju. M. Ermol'ev and Ju. M. Kaniovskii.
Asymptotic properties of some stochastic programming methods with
constant step.
Zh. Vychisl. Mat. i Mat. Fiz., 19(2):356-366, 556, 1979.
Ju. M. Ermol'ev and T.P. Mar'janovic.
Optimization and simulation.
Problemy Kibernet., 27:111-125, 294, 1973.
A collection consisting mainly of papers presented at the Second
All-Union Conference on Problems of Theoretical Cybernetics (Novosibirsk,
1971).
Ju. M. Ermol'ev and I.M. Mel'nik.
Stochastic programming methods with a finite number of tests.
Kibernetika (Kiev), 4:52-54, 1974.
Ju. M. Ermol'ev and F. Mirzoahmedov.
Direct methods of stochastic programming in problems of inventory
planning.
Kibernetika (Kiev), 6:74-81, 1976.
Ju. M. Ermol'ev and E.A. Nurminskii.
Extremal problems of statistics, and numerical methods for stochastic
programming.
In Certain questions on simulation and systems control
(Russian), pages 31-52. Izdat. "Naukova Dumka", Kiev, 1973.
Ju. M. Ermol'ev and N.Z. Sor.
A method of random search for a two step problem of stochastic
programming and its generalization.
Kibernetika (Kiev), 1:90-92, 1968.
Ju. M. Ermol'ev and A.D. Tuniev.
Direct methods of solution of certain problems of stochastic
programming.
Kibernetika (Kiev), 4:100-102, 1968.
Ju. M. Ermol'ev and P.I. Vercenko.
The linearization method in limit extremal problems.
Kibernetika (Kiev), 2:65-69, 1976.
Ju.M. Ermol'ev.
Ueber einige Probleme der stochastischen Programmierung.
Kibernetika, Kiev 1970, No.1, 1-5, 1970.
Ju.M. Ermol'ev.
Ueber ein allgemeines Problem der stochastischen Programmierung.
Kibernetika, Kiev 1971, Nr. 3, 47-50, 1971.
Ju.M. Ermol'ev.
Stochastische Modelle und Optimierungsmethoden.
Kibernetika, Kiev 1975, Nr. 4, 109-119, 1975.
Ju.M. Ermol'ev and T.P. Mar'janovic.
Optimierung und Modellierung.
Probl. Kibernetiki 27, 111-125, 1973.
Ju.M. Ermol'ev and I.M. Mel'nik.
Ueber Methoden der stochastischen Programmierung mit einer endlichen
Zahl von Proben.
Kibernetika, Kiev 1974, Nr. 4, 52-54, 1974.
Ju.M. Ermol'ev and F. Mirzoahmedov.
Direkte Methoden der stochastischen Programmierung in Problemen der
Vorratsplanung.
Kibernetika, Kiev 1976, No.6, 74-81, 1976.
Ju.M. Ermol'ev and A.D. Tuniev.
Random Fejer and quasi-Fejer sequences.
Select. Translat. math. Statist. Probab. 13, 143-148, 1973.
Yu. M. Ermol'ev.
The stochastic quasigradient methods and their application to the
stochastic programming problems with nonsmooth functions.
In Survey of mathematical programming (Proc. Ninth Internat.
Math. Programming Sympos., Budapest, 1976), Vol. 2, pages 107-115,
Amsterdam, 1979. North-Holland.
Yu. M. Ermol'ev and A.A. Gaivoronskii.
Stochastic methods for solving minimax problems.
Cybernetics, 19(4):550-559 (1984), 1983.
Yu. M. Ermol'ev and Ts. Kh. Nedeva.
Questions of the stability of the solution of stochastic programming
problems.
In Methods of investigation of extremal problems, pages 29-34,
120, Kiev, 1981. Akad. Nauk Ukrain. SSR Inst. Kibernet.
Yu. M. Ermol¢ev and V. I. Norkin.
On the nonstationary law of large numbers for dependent random
variables and its application to stochastic optimization.
Kibernet. Sistem. Anal., (4):94-106, 190, 1998.
Yu. M. Ermol¢ev and V. I. Norkin.
The stochastic generalized gradient method for solving nonconvex
nonsmooth problems of stochastic optimization.
Kibernet. Sistem. Anal., (2):50-71, 187, 1998.
Yu.M. Ermol'ev and Yu.M. Kaniovskij.
Asymptotische Eigenschaften gewisser Methoden der stochastischen
Programmierung mit konstantem Schritt.
Zh. Vychisl. Mat. Mat. Fiz. 19, 356-366, 1979.
Yu.M. Ermol'ev and I.N. Kovalenko, editors.
Mathematical methods in operations research and reliability
theory. (Matematicheskie methody issledovaniya operatsij i teorii
nadezhnosti).
Kiev: Institut Kibernetiki AN USSR., 1978.
Yu.M. Ermol'ev, I.I. Lyashko, V.S. Mikhalevich, and V.I. Tyuptya.
Mathematical methods in the investigation of operations.
Textbook for universities. (Matematicheskie metody issledovaniya operatsij.
Uchebnoe posobie dlya vuzov.).
Kiev: Izdatel'skoe Ob'edinenie "Vishcha Shkola"., 1979.
Yu.M. Ermol'ev and Ts.Kh. Nedeva.
Stability questions of the solution for problems of stochastic
programming.
In Investigation methods for extremal problems, Collect.
Artic., Kiev 1981, 29-34, 1981.
Yu.M. Ermol'ev and A.I. Yastremskij.
Stochastic models and methods in economic planning.
(Stokhasticheskie modeli i metody v ehkonomicheskom planirovanii).
Ehkonomiko-Matematicheskaya Biblioteka. Moskva: "Nauka"., 1979.
Ermol'ev, Yu. M.
Metody stokhasticheskogo programmirovaniya.
Izdat. "Nauka", Moscow, 1976.
Optimizatsiyaa i Issledovanie Operatsii. [Monographs in
Optimization and Operations Research].
T.Yu. Ermol'eva.
Asymptotic behavior of regression estimators of stochastic processes
with prior inequality constraints.
Cybernetics 25, No.4, 525-530 translation from Kibernetika 1989,
No.4, 86-89 (1989)., 1989.
Y. M. Ermoliev, T. Y. Ermolieva, G. J. MacDonald, and V. I. Norkin.
Insurability of catastrophic risks: the stochastic optimization
model.
Optimization, 47(3-4):251-265, 2000.
Numerical methods for stochastic optimization and real-time control
of robots (Neubiberg/Munich, 1998).
Y. M. Ermoliev, T. Y. Ermolieva, G. J. MacDonald, and V. I. Norkin.
Stochastic optimization of insurance portfolios for managing exposure
to catastrophic risks.
Ann. Oper Res., 99:207-225 (2001), 2000.
Applied mathematical programming and modeling, IV (Limassol, 1998).
Y.M. Ermoliev.
Aspects of optimization and adaptation.
In Stochastic phenomena and chaotic behaviour in complex systems
(Flattnitz, 1983), volume 21 of Springer Ser. Synergetics, pages
13-16. Springer, Berlin, 1984.
Y.M. Ermoliev and G. Leonardi.
Some proposals for stochastic facility location models.
Math. Modelling, 3(5):407-420, 1982.
Y.M. Ermoliev, V.I. Norkin, and R.J-B. Wets.
The minimization of semicontinuous functions: mollifier subgradients.
SIAM Journal on Control and Optimization, 33(1):149-167, 1995.
Yu. Ermoliev.
Random optimization and stochastic programming.
In Colloq. Methods Optim. Novosibirsk USSR 1968, Lecture Notes
Math. 112, 104-115, 1970.
Yu. Ermoliev.
Stochastic quasigradient methods.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 141-185. Springer, Berlin, 1988.
Yu. Ermoliev, A. Gaivoronski, and C. Nedeva.
Stochastic optimization problems with incomplete information on
distribution functions.
SIAM J. Control Optim., 23(5):697-716, 1985.
Yu. Ermoliev, S. Uryasev, and J. Wessels.
On optimization of unreliable material flow systems.
In Probabilistic constrained optimization, volume 49 of
Nonconvex Optim. Appl., pages 45-66. Kluwer Acad. Publ., Dordrecht, 2000.
Yu. Ermoliev and R. Wets.
Stochastic programming, an introduction.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 1-32. Springer, Berlin, 1988.
Yu. Ermoliev and R.J-B. Wets.
Numerical Techniques for Stochastic Optimization.
Springer-Verlag, Berlin etc., 1988.
Yu. M. Ermoliev.
Methods of nondifferentiable and stochastic optimization and their
applications.
In Progress in nondifferentiable optimization, volume 8 of
IIASA Collaborative Proc. Ser. CP-82, pages 5-27, Laxenburg, 1982.
Internat. Inst. Appl. Systems Anal.
Yuri Ermoliev.
Stochastic quasigradient methods and their application to system
optimization.
Stochastics, 9(1-2):1-36, 1983.
Yuri Ermoliev and Vladimir Norkin.
On constrained discontinuous optimization.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 128-144. Springer, Berlin, 1998.
Yuri Ermoliev and Vladimir Norkin.
Stochastic optimization of risk functions via parametric smoothing.
In Dynamic stochastic optimization (Laxenburg, 2002), volume
532 of Lecture Notes in Econom. and Math. Systems, pages 225-247.
Springer, Berlin, 2004.
Yuri M. Ermoliev and Vladimir I. Norkin.
Normalized convergence in stochastic optimization.
Ann. Oper. Res., 30(1-4):187-198, 1991.
Stochastic programming, Part I (Ann Arbor, MI, 1989).
Yury M. Ermoliev and Alexei A. Gaivoronski.
Stochastic quasigradient methods for optimization of discrete event
systems.
Ann. Oper. Res., 39(1-4):1-39 (1993), 1992.
Yu. M. Ermoljev and E.A. Nurminskiy.
Stochastic quasigradient algorithms for minimax problems in
stochastic programming.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 275-285. Academic Press, London, 1980.
A.N. Ermolov.
Revelation of preferences in a Bayesian game-theoretic model of
social choice.
Dokl. Akad. Nauk, 333(3):293-296, 1993.
G. Escher.
Sequentielle Zuordnungsprobleme.
In Sechste Oberwolfach-Tagung über Operations Research (1973),
Teil II, pages 1-13. Operations Research Verfahren, Band XIX, Hain,
Miesenheim am Glan, 1974.
L. F. Escudero, C. Garcia, J. L. de la Fuente, and F. J. Prieto.
Hydropower generation management under uncertainty via scenario
analysis and parallel computation.
IEEE Transactions on Power Systems, 11(2):683-689, 1996.
L. F. Escudero, A. Garín, M. Merino, and G. Pérez.
A two-stage stochastic integer programming approach as a mixture of
branch-and-fix coordination and Benders decomposition schemes.
Ann. Oper. Res., 152:395-420, 2007.
L. F. Escudero, I. Paradinas, and F. J. Prieto.
Generation expansion planning under uncertainty in demand, economic
environment, generation availability and book life.
In Proceedings of the IEEE Stockholm Power Tech, pages
226-233, Stockholm, Sweden, 1995.
L. F. Escudero, F. J. Quintana, and J. Salmeron.
CORO, a modeling and an algorithmic framework for oil supply,
transformation and distribution optimization under uncertainty.
European Journal of Operational Research, 114(3):638-656,
1999.
L. F. Escudero, J. Salmeron, I. Paradinas, and M. Sanchez.
SEGEM: A simulation approach for electric generation management.
IEEE Transactions on Power Systems, 13(3):738-748, 1998.
Laureano F. Escudero, Araceli Garín, María Merino, and Gloria
Pérez.
The value of the stochastic solution in multistage problems.
TOP, 15(1):48-64, 2007.
Laureano F. Escudero, Pasumarti V. Kamesam, Alan J. King, and Roger J.-B. Wets.
Production planning via scenario modelling.
Ann. Oper. Res., 43(1-4):311-335, 1993.
Applied mathematical programming and modelling (Uxbridge, 1991).
L.F. Escudero and P.V. Kamesam.
On solving stochastic production planning problems via scenario
modelling.
Top, 3(1):69-95, 1995.
Augustine O. Esogbue and Amar J. Singh.
A stochastic model for an optimal priority bed distribution problem
in a hospital ward.
Operations Res. 24, 884-898, 1976.
Juan Estalrich and Nathan Buras.
Alternative specifications of state variables in
stochastic-dynamic-programming models of reservoir operation.
Appl. Math. Comput., 44(2, part II):143-155, 1991.
Alexander Ettinger and Peter L. Hammer.
Pseudo-Boolean programming with random coefficients.
Cahiers Centre Études Recherche Opér., 14:67-82, 1972.
R. Everitt and W.T. Ziemba.
Two-period stochastic programs with simple recourse.
Oper. Res., 27(3):485-502, 1979.
A. Evgrafov and M. Patriksson.
On the existence of solutions to stochastic mathematical programs
with equilibrium constraints.
J. Optim. Theory Appl., 121(1):65-76, 2004.
I. V. Evstigneev and M. I. Taksar.
Convex stochastic optimization for random fields on graphs: a method
of constructing Lagrange multipliers.
Math. Methods Oper. Res., 54(2):217-237, 2001.
Igor V. Evstigneev and Sjur D. Flåm.
Convex stochastic duality and the "biting lemma".
J. Convex Anal., 9(1):237-244, 2002.
Igor V. Evstigneev and Priscilla E. Greenwood.
Stochastic extrema, splitting random elements and models of crack
formation.
In System modelling and optimization (Compiègne, 1993),
volume 197 of Lecture Notes in Control and Inform. Sci., pages
315-319. Springer, London, 1994.
I.V. Evstigneev.
Optimal stochastic programs and their stimulating prices.
In Math. Models Econ., Proc. Sympos. math. Meth. Econ. and
Conf. von Neumann Models, Warszawa 1972, 219-252, 1974.
I.V. Evstigneev.
Lagrange multipliers for the problems of stochastic programming.
In Warsaw Fall Semin. math. Econ. 1975, Lect. Notes Econ. math.
Syst. 133, 34-48, 1976.
I.V. Evstigneev.
Turnpike theorems in stochastic models of economic dynamics.
Mat. Zametki (Math. Notes) (translated into English), v.19, n.2,
279-290, 1976.
I.V. Evstigneev.
Homogeneous convex models in the theory of controlled random
processes.
Doklady AN SSSR (Soviet Math. Dokl.) (translated into English),
v.253, n.3, 524-527, 1980.
I.V. Evstigneev.
Measurable selection theorems and stochastic control models in
general topological spaces.
Mat. Sbornik (Math. USSR Sbornik) (translated into English),
v.131, n.1, 27-39, 1986.
I.V. Evstigneev.
Controlled random fields on a directed graph.
Teor. Ver. i Primen. (Theory of Probab. and Appl.) (translated
into English), v.33, n.3, 465-479, 1988.
I.V. Evstigneev.
Stochastic extremal problems and the strong Markov property of
random fields.
Uspekhi Matem. Nauk (Russian Math. Surveys) (translated into
English), vol. 43, nr. 2, 3-41, 1988.
I.V. Evstigneev.
The shortest path around an island outside the shallows.
Markov Processes and Related Fields, v.1, 407-418, 1995.
I.V. Evstigneev and V.I. Arkin.
Stochastic models of control and economic dynamics.
Academic Press, London, 1987.
I.V. Evstigneev and S.D. Flaam.
The turnpike property and the central limit theorem in stochastic
models of economic dynamics.
In Yu.M. Kabanov, B.L. Rozovskii, and A.N. Shiryaev, editors,
Statistics and Control of Stochastic Processes, pages 63-101. World
Scientific, Singapore - New Jersey - London, 1997.
I.V. Evstigneev and P.E. Greenwood.
Markov fields over countable partially ordered sets: Extrema and
splitting.
Memoirs of Amer. Math. Soc., v. 112 (537), 1994.
I.V. Evstigneev and M.I. Taksar.
Stochastic equilibria on graphs, I.
Journal of Math. Economics, v. 23, 401-433, 1994.
I.V. Evstigneev and M.I. Taksar.
Stochastic equilibria on graphs, II.
Journal of Math. Economics, v. 24, 383-406, 1995.
James B. Ewbank, Bob L. Foote, and Hillel J. Kumin.
A method for the solution of the distribution problem of stochastic
linear programming.
SIAM J. Appl. Math., 26:225-238, 1974.
Eweda Eweda and Odile Macchi.
Convergence of an adaptive linear estimation algorithm.
IEEE Trans. Autom. Control AC-29, 119-127, 1984.
¯I. ¯I. ¯Ezov and Hoang Sum.
Maximization processes with discrete time.
Teor. Verojatnost. i Mat. Statist., 9:82-89, 175, 1973.
B. A. Faber and J. R. Stedinger.
Reservoir optimization using sampling sdp with ensemble streamflow
prediction (esp) forecasts.
Journal of Hydrology, 249(1-4):113-133, 2001.
Malte Michael Faber.
Stochastisches Programmieren.
Wuerzburg-Wien: Physica-Verlag., 1970.
C. Fabian and M. Stoica.
Fuzzy integer programming.
In Fuzzy sets and decision analysis, TIMS Stud. Manage. Sci.
20, 123-131 , 1984.
Csaba I. Fábián.
Adapting an approximate level method to the two-stage stochastic
programming problem.
Stochastic Programming E-Print Series, http://www.speps.org,
2001.
Csaba I. Fabian.
Decomposing cvar minimization in two-stage stochastic models.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Csaba I. Fábián, Richárd Némedi, and Zoltán Szöke.
A stochastic programming model for optical fiber manufacturing.
CEJOR Cent. Eur. J. Oper. Res., 9(4):343-359, 2001.
Csaba I. Fábián, András Prékopa, and Olga Ruf-Fiedler.
On a dual method for a specially structured linear programming
problem with application to stochastic programming.
Optim. Methods Softw., 17(3):445-492, 2002.
Stochastic programming.
Csaba I. Fabian and Anna Veszpremi.
Algorithms for handling cvar-constraints in dynamic stochastic
programming models with applications to finance.
Stochastic Programming E-Print Series, http://www.speps.org,
2007.
V. Fabian.
Simulated annealing simulated.
Comput. Math. Appl., 33(1-2):81-94, 1997.
Approximation theory and applications.
Vaclav Fabian.
A local asymptotic minimax optimality of an adaptive Robbins-Monro
stochastic approximation procedure.
In Mathematical learning models - theory and algorithms, Proc.
Conf., Bad Honnef/Ger. 1982, Lect. Notes Stat. 20, 43-49, 1983.
Ulrich Faigle and Alexander Schoenhuth.
Note on Negative Probabilities and Observable Processes.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Ulrich Faigle and Rainer Schrader.
On the convergence of stationary distributions in simulated
annealing algorithms.
Inf. Process. Lett. 27, No.4, 189-194, 1988.
Shu-Kai S. Fan and Erwie Zahara.
Stochastic response surface optimization via an enhanced
Nelder-Mead simplex search procedure.
Eng. Optim., 38(1):15-36, 2006.
Yu Bin Fan.
The Bayesian statistical method for estimating the OD trip
matrix from link volumes.
Tongji Daxue Xuebao Ziran Kexue Ban, 19(2):227-233, 1991.
Zhen Fan.
Stochastic programming model with CVaR-recourse criterion for
credit portfolio optimization.
Comm. Appl. Math. Comput., 20(1):56-62, 2006.
G. Fandel and J. Wilhelm.
Rational solution principles and information requirements as
elements of a theory of multiple criteria decision making. With a comment by
P. Hansen.
In Multiple Criteria Decis. Making, Proc. Conf. Jouy-en-Josas
1975, Lect. Notes Econ. Math. Syst. 130, 215-231, 1976.
H. T. Fang and H. F. Chen.
Almost surely convergent global optimization algorithm using
noise-corrupted observations.
J. Optim. Theory Appl., 104(2):343-376, 2000.
Kai-Tai Fang, Dietmar Maringer, Yu Tang, and Peter Winker.
Lower bounds and stochastic optimization algorithms for uniform
designs with three or four levels.
Math. Comp., 75(254):859-878 (electronic), 2006.
Kai Tai Fang and Yuan Wang.
A sequential algorithm for optimization and its applications to
regression analysis.
In Lecture notes in contemporary mathematics, 1989, pages
17-28. Science Press, Beijing, 1990.
Gino Favero.
Shortfall risk minimization under model uncertainty in the binomial
case: adaptive and robust approaches.
Math. Methods Oper. Res., 53(3):493-503, 2001.
I.K. Fedorenko and V.I. Rymaruk.
Some approaches to solving stochastic allocation problems.
Issled. Operatsii i ASU, 26:3-6, 116, 1985.
V.V. Fedorov.
Stochastic decomposition in extremal problems.
Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet.,
1:52-57, 73, 1980.
A.N. Fedotov.
An algorithm for the solution of a class of problems of stochastic
programming.
In Dynamics of non-homogeneous systems, Mater. Semin., Moskva
1983, 130-140 , 1983.
A.V. Fedotov.
An algorithm for the solution of M-problems of stochastic
programming.
In Methods of the investigation of complex systems, Proc.
Conf., Moskva 1985, 30-35, 1985.
Sergei Fedotov and Sergei Mikhailov.
Option pricing for incomplete markets via stochastic optimization:
transaction costs, adaptive control and forecast.
Int. J. Theor. Appl. Finance, 4(1):179-195, 2001.
A.A. Fedulov, Yu.G. Fedulov, and V.N. Tsygichko.
Introduction to the theory of statistically non-reliable
solutions. (Vvedenie v teoriyu statisticheski nenadezhnykh reshenij).
Moskva: "Statistika"., 1979.
E.A. Feinberg.
Parametric stochastic dynamic programming.
In Statistics and control of stochastic processes. Vol. 2, Pap.
Steklov Semin., Moscow/USSR 1985-86, Transl. Ser. Math. Eng., 103-120,
1989.
Eugene A. Feinberg and Adam Shwartz, editors.
Handbook of Markov decision processes, volume 40 of
International Series in Operations Research & Management Science.
Kluwer Academic Publishers, Boston, MA, 2002.
Methods and applications.
B. R. Feiring, T. Sastri, and L. S. M. Sim.
A stochastic programming model for water resource planning.
Mathematical and Computer Modelling, 27(3):1-7, 1998.
L.I. Fejgin.
Ein Zuordnungsproblem bei unvollstaendiger Information ueber die
Gestehungskosten von Operationen.
Izv. Akad. Nauk SSSR, Tekh. Kibern. 1970, No.6, 33-40, 1970.
Sándor Fekete, Rolf Klein, and Andreas Nüchter.
Searching with an Autonomous Robot.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Paul Feldman and Silvio Micali.
An optimal probabilistic algorithm for synchronous Byzantine
agreement.
In Automata, languages and programming, Proc. 16th Int.
Colloq., Stresa/Italy 1989, Lect. Notes Comput. Sci. 372, 341-378, 1989.
M. Fels, D.S. Lycon, D.J. Meeuwig, J.W. Meeuwig, and J.D. Pinter.
An intelligent decision support system for assisting industrial
wastewater management.
Annals of Operations Research, 58:455-477, 1995.
En Min Feng, Guang Yan Shi, Gui Qing Gao, and Huan Wen Tan.
Application of stochastic programming in the design and management of
a special purpose wharf.
Math. Practice Theory, 1:9-14, 1984.
Afonso G. Ferreira and Janez Zerovnik.
Bounding the probability of success of stochastic methods for global
optimization.
Comput. Math. Appl., 25(10-11):1-8, 1993.
Michael C. Ferris and Andrzej Ruszczy\'nski.
Robust path choice in networks with failures.
Networks, 35(3):181-194, 2000.
Miroslav Fiedler.
Doubly stochastic matrices and optimization.
In Advances in mathematical optimization, Pap. Dedic. F.
Nozicka Occas. 70. Birthday, Math. Res. 45, 44-51, 1988.
Olga Fiedler and Werner Römisch.
Stability in multistage stochastic programming.
Ann. Oper. Res., 56:79-93, 1995.
Stochastic programming (Udine, 1992).
S. Filippi and S. Czakay.
Ein modifiziertes Hooke-Jeeves-Verfahren und ein modifiziertes
Choit- Schrack-Verfahren zur nichtlinearen Optimierung.
Math. Operationsforsch. Stat., Ser. Optimization 13, 419-429,
1982.
Ulrich Fincke and Horst W. Hamacher.
On the expected value of stochastic linear programs and (dynamic)
network flow problems.
European J. Oper. Res., 29(3):307-316, 1987.
Charles H. Fine and Robert M. Freund.
Optimal investment in product-flexible manufacturing capacity.
Manage. Sci. 36, No.4, 449-466, 1990.
P. Fischer and E.R. Swart.
Three-dimensional line stochastic matrices and extreme points.
Linear Algebra Appl., 69:179-203, 1985.
I.H. Fisher.
Derivation of optimal stocking policies for grazing in arid regions.
I. Methodology.
Appl. Math. Comput. 17, 1-35, 1985.
George S. Fishman and David S. Rubin.
Bounding the variance in Monte Carlo experiments.
Oper. Res. Lett., 11(4):243-248, 1992.
Sjur D. Flaam and Alberto Seeger.
Solving cone-constrained convex programs by differential
inclusions.
Math. Program. 65A, No.1, 107-121, 1994.
S.D. Flåm.
Nonanticipativity in stochastic programming.
J. Optim. Theory Appl., 46(1):23-30, 1985.
S.D. Flam and J.D. Pinter.
Selecting oil exploration strategies: Some stochastic programming
formulations and solution methods.
Research Report CMI 852611-1, Christian Michelsen Institute, Fantoft,
Bergen, Norway, 1985.
S.D. Flåm and J. Zowe.
Exact penalty functions in single-stage stochastic programming.
Optimization, 21(5):723-734, 1990.
Sjur D. Flåm.
Asymptotically stable solutions to stochastic optimization problems.
In Stochastic programming (Gargnano, 1983), volume 76 of
Lecture Notes in Control and Inform. Sci., pages 184-193. Springer, Berlin,
1986.
Sjur D. Flåm.
Shadow prices in stochastic programming: their existence and
significance.
In Stochastic models and option values (Loen, 1989), volume 200
of Contrib. Econom. Anal., pages 227-240. North-Holland, Amsterdam,
1991.
Sjur D. Flam.
Finite convergence in stochastic programming.
In Stochastic optimization. Numerical methods and technical
applications, Proc. GAMM/IFIP-Workshop, Neubiberg/Ger. 1990, Lect. Notes
Econ. Math. Syst. 379, 1-14, 1992.
Sjur D. Flåm.
Lagrange multipliers in stochastic programming.
SIAM J. Control Optim., 30(1):1-10, 1992.
Sjur D. Flåm and Rüdiger Schultz.
A new approach to stochastic linear programming.
Numer. Funct. Anal. Optim., 14(5-6):545-554, 1993.
Sjur D. Flåm and Roger J.-B. Wets.
Finite horizon approximates of infinite horizon stochastic programs.
In Stochastic optimization (Kiev, 1984), volume 81 of
Lecture Notes in Control and Inform. Sci., pages 339-350. Springer, Berlin,
1986.
Sjur Didrik Flåm.
Corrigendum: "Lagrange multipliers in stochastic programming"
[SIAM J. Control. Optim. 30 (1992), no. 1, 1-10;
MR 93c:90051].
SIAM J. Control Optim., 33(2):667-671, 1995.
Sjur Didrik Flåm.
Optimization under uncertainty using momentum.
In Dynamic stochastic optimization (Laxenburg, 2002), volume
532 of Lecture Notes in Econom. and Math. Systems, pages 249-256.
Springer, Berlin, 2004.
Lisa Fleischer, Jochen Könemann, Stefano Leonardi, and Guido Schäfer.
Simple cost sharing schemes for multicommodity rent-or-buy and
stochastic Steiner tree.
In STOC'06: Proceedings of the 38th Annual ACM Symposium on
Theory of Computing, pages 663-670, New York, 2006. ACM.
Stein-Erik Fleten, Kjetil Høyland, and Stein W. Wallace.
The performance of stochastic dynamic and fixed mix portfolio models.
European J. Oper. Res., 140(1):37-49, 2002.
Stein-Erik Fleten and Trine Krogh Kristoffersen.
Short-term hydropower production planning by stochastic programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
Stein-Erik Fleten and Trine Krogh Kristoffersen.
Stochastic programming for optimizing bidding strategies of a nordic
hydropower producer.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
Stein-Erik Fleten and Snorre Lindset.
Optimal hedging strategies for multi-period guarantees in the
presence of transaction costs: A stochastic programming approach.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
Stein-Erik Fleten and Erling Pettersen.
Optimization of physical purchasing for a price-taking retailer in
the norwegian electricity market.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Stein-Erik Fleten, Stein W. Wallace, and William T. Ziemba.
Hedging electricity portfolios via stochastic programming.
Stochastic Programming E-Print Series, http://www.speps.org,
1999.
Stein-Erik Fleten, Stein W. Wallace, and William T. Ziemba.
Hedging electricity portfolios via stochastic programming.
In Decision making under uncertainty, volume 128 of IMA
Vol. Math. Appl., pages 71-93. Springer, New York, 2002.
C.J. Fodor and J.D. Pinter.
Extreme order statistics applied for optimum estimation in 'hard'
MP problems.
In Transactions of the Tenth Prague Conference on Information
Theory, Statistical Decision Functions, and Random Processes. (Prague, July,
1986), pages 321-328, Prague, 1988. Publishing House of the Czechoslovakian
Academy of Sciences.
János C. Fodor and János Pintér.
Extreme order statistics applied for optimum estimation in "hard"
MP problems.
In Transactions of the Tenth Prague Conference on Information
Theory, Statistical Decision Functions, Random Processes, Vol. A (Prague,
1986), pages 321-328. Reidel, Dordrecht, 1988.
Renato Fonso.
On a stochastic transportation problem.
In Proceedings of the First AMASES Meeting (Pisa, 1977)
(Italian), pages 149-182, Turin, 1979. Giappichelli.
Renato Fonso.
On the convergence of a feasible directions algorithm for
optimization problems with linear constraints and strictly convex
non-differentiable objective function.
Riv. Mat. Sci. Econ. Soc. 5, 115-122, 1982.
B.L. Foote.
A discussion of the properties of a basic simplex algorithm for
generating the decision regions of Bereanu.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 207-222, London, 1980. Academic Press.
Jean-Pierre Forestier.
Optimisation de la commande des systemes a representation semi-
markovienne.
In Mathematiques appliquees, 1er Colloq. AFCET-SMF, Palaiseau
1978, Tome I, 337-346, 1978.
O. B. Fosso, A. Gjelsvik, A. Haugstad, B. Mo, and I. Wangensteen.
Generation scheduling in a deregulated system. The Norwegian
case.
IEEE Transactions on Power Systems, 14(1):75-80, 1999.
Robert Fourer and Leo Lopes.
A management system for decompositions in stochastic programming.
Ann. Oper. Res., 142:99-118, 2006.
Robert Fourer and Leo Lopes.
StAMPL: A filtration-oriented modeling tool for stochastic
programming.
Optimization Online, http://www.optimization-online.org, 2006.
L. Fourgeaud and S. Fourgeaud.
Critere de choix en avenir partiellement incertain.
Rev. Franc. Inform. Rech. Oper. 2, No.14, 9-19, 1968.
E. Fragniere and A. Haurie.
A stochastic programming model for energy/environment choices under
uncertainty.
International Journal of Environment and Pollution,
6(4-6):587-603, 1996.
In Georghe, A. V. (ed), "Integrated Regional Health and
Environmental Risk Assessment and Safety Management".
Emmanuel Fragnière and Jacek Gondzio.
Stochastic programming from modeling languages.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 95-113. SIAM, Philadelphia, PA, 2005.
Emmanuel Fragnière, Jacek Gondzio, and Jean-Philippe Vial.
Building and solving large-scale stochastic programs on an affordable
distributed computing system.
Ann. Oper Res., 99:167-187 (2001), 2000.
Applied mathematical programming and modeling, IV (Limassol, 1998).
P. M. Franca and H. P. L. Luna.
Solving stochastic transportation-location problems by generalized
Benders decomposition.
Transportation Science, 16(2):113-126, 1982.
Linos F. Frantzeskakis and Warren B. Powell.
A successive linear approximation procedure for stochastic, dynamic
vehicle allocation problems.
Transportation Sci., 24(1):40-57, 1990.
Linos F. Frantzeskakis and Warren B. Powell.
Bounding procedures for multistage stochastic dynamic networks.
Networks, 23(7):575-595, 1993.
Linos F. Frantzeskakis and Warren B. Powell.
Restricted recourse strategies for bounding the expected network
recourse function.
Ann. Oper. Res., 64:261-287, 1996.
Stochastic programming, algorithms and models (Lillehammer, 1994).
Astrid Franz and Karl Heinz Hoffmann.
Optimal annealing schedules for a modified Tsallis statistics.
J. Comput. Phys., 176(1):196-204, 2002.
K. Frauendorfer.
Bounding the expectation of a function of a multivariate random
variable-with application to stochastic programming.
In XI symposium on operations research (Darmstadt, 1986),
volume 57 of Methods Oper. Res., pages 13-23.
Athenäum/Hain/Hanstein, Königstein/Ts., 1987.
K. Frauendorfer.
Solving SLP recourse problems: the case of stochastic
technology matrix, RHS & objective.
In System modelling and optimization (Tokyo, 1987), volume 113
of Lecture Notes in Control and Inform. Sci., pages 90-100. Springer,
Berlin, 1988.
K. Frauendorfer.
Stochastic dynamic optimization: modelling and methodological
aspects.
In System modelling and optimization (Compiègne, 1993),
volume 197 of Lecture Notes in Control and Inform. Sci., pages
332-341. Springer, London, 1994.
K. Frauendorfer and P. Kall.
A solution method for SLP recourse problems with arbitrary
multivariate distributions-the independent case.
Problems Control Inform. Theory/Problemy Upravlen. Teor.
Inform., 17(4):177-205, 1988.
K. Frauendorfer and Ch. Marohn.
Refinement issues in stochastic multistage linear programming.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 305-328. Springer, Berlin, 1998.
Karl Frauendorfer.
Solving SLP recourse problems with arbitrary multivariate
distributions-the dependent case.
Math. Oper. Res., 13(3):377-394, 1988.
Karl Frauendorfer.
On the value of perfect information and approximate solutions in
convex stochastic two-stage optimization.
In System modelling and optimization (Zurich, 1991), volume 180
of Lecture Notes in Control and Inform. Sci., pages 564-573. Springer,
Berlin, 1992.
Karl Frauendorfer.
Stochastic two-stage programming, volume 392 of Lecture
Notes in Economics and Mathematical Systems.
Springer-Verlag, Berlin, 1992.
Habilitationsschrift, University of Zürich, Zürich, 1992.
Karl Frauendorfer.
The approximation of separable stochastic programs.
J. Comput. Appl. Math., 56(1-2):23-44, 1994.
Stochastic programming: stability, numerical methods and applications
(Gosen, 1992).
Karl Frauendorfer.
Multistage stochastic programming: error analysis for the convex
case.
Z. Oper. Res., 39(1):93-122, 1994.
Karl Frauendorfer.
Barycentric scenario trees in convex multistage stochastic
programming.
Math. Programming, 75(2, Ser. B):277-293, 1996.
Approximation and computation in stochastic programming.
Karl Frauendorfer and Jens Güssow.
Stochastic multistage programming in the operation and management of
a power system.
In Stochastic optimization techniques (Neubiberg/Munich, 2000),
volume 513 of Lecture Notes in Econom. and Math. Systems, pages
199-222. Springer, Berlin, 2002.
Karl Frauendorfer and Gido Haarbrücker.
Test problems in stochastic multistage programming.
Optimization, 47(3-4):267-285, 2000.
Numerical methods for stochastic optimization and real-time control
of robots (Neubiberg/Munich, 1998).
Karl Frauendorfer and Michael Schürle.
Term structure models in multistage stochastic programming:
estimation and approximation.
Ann. Oper Res., 100:189-209 (2001), 2000.
Research in stochastic programming (Vancouver, BC, 1998).
James R. Freeland and Gerhard Schiefer.
Using probabilistic information in solving resource allocation
problems for a decentralized firm.
Internat. J. Policy Anal. Inform. Systems, 5(4):325-340, 1981.
P. Freeman and A.E. Gear.
A probabilistic objective function for R P D portfolio selection.
Operat. Res. Quart. 22, 253-265, 1971.
Michael Freimer, Doug Thomas, and Jeff Linderoth.
Reducing bias in stochastic linear programming with sampling methods.
Optimization Online, http://www.optimization-online.org, 2005.
J.B.G. Frenk, A.H.G. Rinnooy Kan, and L. Stougie.
A hierarchical scheduling problem with a well-solvable second stage.
Annals of Operations Research, 1:43-58, 1984.
H. Frick.
On the solution of the facility location problem when the dominance
method fails.
Z. Oper. Res., Ser. B 29, B101-B106, 1985.
Bozena Friedrich and Klaus Tammer.
Untersuchungen zur Stabilität einiger Ersatzprobleme der
stochastischen Optimierung in Bezug auf änderungen des zugrunde
gelegten Zufallsvektors.
Wiss. Z. Humboldt-Univ. Berlin Math.-Natur. Reihe,
30(5):373-376, 1981.
A.M. Frieze.
An algorithm for finding Hamilton cycles in random directed graphs.
J. Algorithms 9, No.2, 181-204, 1988.
Klaus Fritzsch.
On the acceleration of adaptation processes by two-step principles.
Kybernetika, Praha 13, 274-281, 1977.
D.D. Frolkin.
A stochastic problem of planning storage and irrigation.
Zh. Vychisl. Mat. i Mat. Fiz., 21(3):595-604, 810, 1981.
D.D. Frolkin.
The solution of some stochastic two-stage problems.
U.S.S.R. Comput. Math. Math. Phys. 24, No.3, 123-128 translation
from Zh. Vychisl. Mat. Mat. Fiz. 24, No.6, 823-830 (1984)., 1984.
E. Frostig and I. Adiri.
Stochastic flowshop no-wait scheduling.
J. Appl. Probab. 22, 240-246, 1985.
Bor-Ruey Fu.
Modeling and analysis of discrete tandem production lines using
continuous flow models.
Ph.D. Dissertation, Department of Industrial Engineering,
University of Wisconsin-Madison, Madison, WI, 1996.
M.C. Fu.
Convergence of a stochastic approximation algorithm for the GI/G/1
queue using infinitesimal perturbation analysis.
J. Optimization Theory Appl. 65, No.1, 149-160, 1990.
M.C. Fu and S.D. Hill.
Optimization of discrete event systems via simultaneous perturbation
stochastic approximation.
Transactions of the Institute of Industrial Engineers,
29:233-243, 1997.
Michael C. Fu.
Optimization via simulation: a review.
Ann. Oper. Res., 53:199-247, 1994.
Simulation and modeling.
Toshiharu Fujita and Kazuyoshi Tsurusaki.
Optimizing the expectation of negative multiplicative functions.
S¯urikaisekikenky¯usho K¯oky¯uroku, 978:160-172, 1997.
Optimization methods for mathematical systems with uncertainty
(Japanese) (Kyoto, 1996).
Toshiharu Fujita and Kazuyoshi Tsurusaki.
Stochastic optimization of multiplicative functions with negative
value.
J. Oper. Res. Soc. Japan, 41(3):351-373, 1998.
Hiroshi Fujiwara and Kazuo Iwama.
Average-Case Competitive Analyses for Ski-Rental Problems.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Takeshi Fukao and Tetsuya Harada.
Decomposition of objective function in stochastic combinatorial
optimization.
In System modelling and optimization (Leipzig, 1989), volume
143 of Lecture Notes in Control and Inform. Sci., pages 599-610.
Springer, Berlin, 1990.
Kazuma Fukuda, Hitoshi Hagiwara, and Mario Nakamori.
Analysis of probabilistic algorithms for the knapsack problem.
S¯urikaisekikenky¯usho K¯oky¯uroku, 947:162-171, 1996.
Optimization theory in mathematical models (Japanese) (Kyoto, 1995).
Masao Fukushima.
A fixed point approach to certain convex programs with applications
in stochastic programming.
Math. Oper. Res. 8, 517-524, 1983.
R.I. Furunziev and A.Ja. Ismailov.
Eine experimentelle Untersuchung von Algorithmen der stochastischen
Optimierung dynamischer Systeme.
Wiss. Z. Techn. Hochschule Ilmenau 25, No.1, 173-180, 1979.
A. Futschik and G. Pflug.
Confidence sets for discrete stochastic optimization.
Ann. Oper. Res., 56:95-108, 1995.
Stochastic programming (Udine, 1992).
R. Gabasov and V.G. Medvedev.
An algorithm for solving a two-stage linear stochastic programming
problem with several random parameters.
Dokl. Akad. Nauk BSSR, 32(1):13-16, 92, 1988.
J. M. Gablonsky and C. T. Kelley.
A locally-biased form of the DIRECT algorithm.
J. Global Optim., 21(1):27-37, 2001.
Manal Gabour, Simeon Reich, and Alexander J. Zaslavski.
Generic convergence of algorithms for solving stochastic feasibility
problems.
In Inherently parallel algorithms in feasibility and
optimization and their applications (Haifa, 2000), volume 8 of Stud.
Comput. Math., pages 279-295. North-Holland, Amsterdam, 2001.
Norman Gaither.
An experimental solution of the general stochastic programming
problem.
Simulation 30, 191-195, 1978.
V.G. Gaitsgori and A.A. Pervozvanskii.
Approximate optimality of sliding planning.
Automat. Remote Control, 38(10, part 2):1505-1511 (1978),
1977.
A. Gaivoronski.
Linearization methods for optimization of functionals which depend on
probability measures.
Math. Programming Stud., 28:157-181, 1986.
Stochastic programming 84. II.
A. Gaivoronski.
Stochastic optimization techniques for finding optimal submeasures.
In Stochastic optimization (Kiev, 1984), volume 81 of
Lecture Notes in Control and Inform. Sci., pages 351-363. Springer, Berlin,
1986.
A. Gaivoronski.
Numerical techniques for finding estimates which minimize the upper
bound of the absolute deviation.
In Statistical data analysis based on the L\sb 1-norm and
related methods (Neuch sp atel, 1987), pages 247-262, Amsterdam, 1987.
North-Holland.
A. Gaivoronski.
Stochastic quasigradient methods and their implementation.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 313-351. Springer, Berlin, 1988.
A. Gaivoronski, E. Messina, and A. Sciomachen.
A stochastic optimization approach for robot scheduling.
Ann. Oper. Res., 56:109-133, 1995.
Stochastic programming (Udine, 1992).
A.A. Gaivoronski and E. Messina.
Stochastic optimization algorithms for regenerative DEDS.
In System modelling and optimization (Compiègne, 1993),
volume 197 of Lecture Notes in Control and Inform. Sci., pages
320-331. Springer, London, 1994.
A.A. Gaivoronski, E. Messina, and A. Sciomachen.
A statistical generalized programming algorithm for stochastic
optimization problems.
Ann. Oper. Res., 58:297-321, 1995.
Applied mathematical programming and modeling, II (APMOD 93)
(Budapest, 1993).
Alexei A. Gaivoronski.
A numerical method for solving stochastic programming problems with
moment constraints on a distribution function.
Ann. Oper. Res., 31(1-4):347-369, 1991.
Stochastic programming, Part II (Ann Arbor, MI, 1989).
Alexei A. Gaivoronski.
SQG: software for solving stochastic programming problems with
stochastic quasi-gradient methods.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 37-60. SIAM, Philadelphia, PA, 2005.
Alexei A. Gaivoronski.
Stochastic optimization problems in telecommunications.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 669-704. SIAM, Philadelphia, PA, 2005.
Alexei A. Gaivoronski and Petter E. de Lange.
An asset liability management model for casualty insurers: complexity
reduction vs. parameterized decision rules.
Ann. Oper Res., 99:227-250 (2001), 2000.
Applied mathematical programming and modeling, IV (Limassol, 1998).
Alexei A. Gaivoronski, Kjetil Høyland, and Petter E. de Lange.
Statutory regulation of casualty insurance companies: an example from
Norway with stochastic programming analysis.
In Stochastic optimization: algorithms and applications
(Gainesville, FL, 2000), volume 54 of Appl. Optim., pages 55-85.
Kluwer Acad. Publ., Dordrecht, 2001.
Alexei A. Gaivoronski and Fabio Stella.
Stochastic nonstationary optimization for finding universal
portfolios.
Ann. Oper Res., 100:165-188 (2001), 2000.
Research in stochastic programming (Vancouver, BC, 1998).
A.A. Gaivoronskii.
Some methods for the solution of a nonstationary stochastic
programming problem with constraints.
In Methods of operations research and of reliability theory in
systems analysis (Russian), pages 70-79, Kiev, 1977. Akad. Nauk. Ukrain.
SSR Inst. Kibernet.
A.A. Gaivoronskii.
Identification approach to problems of stochastic programming with
independent tests.
In Mathematical methods in operations research and reliability
theory (Russian), pages 87-91, Kiev, 1978. Akad. Nauk Ukrain. SSR Inst.
Kibernet.
A.A. Gaivoronskii.
A nonstationary stochastic programming problem with changing
constraints.
In Mathematical methods in operations research and reliability
theory (Russian), pages 24-30. Akad. Nauk Ukrain. SSR Inst. Kibernet.,
Kiev, 1978.
A.A. Gaivoronskii.
Approximation methods of solution of stochastic programming problems.
Cybernetics, 18(2):241-249, 1982.
A.A. Gaivoronskii.
Numerical methods for minimizing convex functionals from probability
measures with applications to optimal monitoring.
Kibernetika (Kiev), 4:43-51, 134, 1989.
A.A. Gaivoronskii.
A numerical method for searching for optimal submeasures.
Kibernetika (Kiev), 2:70-77, 136, 1990.
A.A. Gaivoronskii and Le Din' Fung.
Solution of nonstationary and limit stochastic inequalities.
Issled. Operatsii i ASU, 13:98-104, 135, 1979.
A.A. Gajvoronskij.
Methods of search of optimal sets.
Cybernetics 14, 731-735 translation from Kibernetika 1978, No.5,
83-86 (1978)., 1979.
A.A. Gajvoronskij.
Numerical minimization methods for convex functionals dependent on
probability measures with applications to optimal pollution monitoring.
Cybernetics 25, No.4, 471-482 translation from Kibernetika 1989,
No.4, 43-51 (1989)., 1989.
A.A. Gajvoronskij.
A numerical method to find optimal submeasures.
Cybernetics 26, No.2, 234-246 translation from Kibernetika 1990,
No.2, 70-77 (1990)., 1990.
A.D. Gajvoronskij and Yu.M. Ermol'ev.
Methods of finding optimal submeasures (characterization of
solutions).
Cybernetics 23, No.5, 684-694 translation from Kibernetika 1987,
No.5, 94-101 (1987)., 1987.
Alexei A. Gajvoronskij.
Optimization of stochastic discrete event dynamic systems: A survey
of some recent results.
In Simulation and optimization, Proc. Int. Workshop Comput.
Intensive Methods Simulation Optimization, Laxenburg/Austria 1990, Lect.
Notes Econ. Math. Syst. 374, 24-44, 1992.
Shmuel Gal and Charles A. Micchelli.
Optimal sequential and nonsequential procedures for evaluating a
functional.
Applicable Anal., 10(2):105-120, 1980.
Tomas Gal, editor.
Grundlagen des Operations Research. 3: Spieltheorie, Dynamische
Optimierung, Lagerhaltung, Warteschlangentheorie, Simulation, Unscharfe
Entscheidungen. Mit Beitr. von M. J. Beckmann, H. Gehring, K.-P. Kistner, Ch.
Schneeweiss, G. Schwoediauer, H.-J. Zimmermann.
Springer-Verlag, 1987.
Tomas Gal and Hartmut Wolf.
Solving stochastic linear programs via goal programming.
In Decision making with multiple objectives (Cleveland, Ohio,
1984), volume 242 of Lecture Notes in Econom. and Math. Systems, pages
126-143. Springer, Berlin, 1985.
Giulia Galbiati.
On the asymptotic probabilistic analysis of scheduling problems in
the presence of precedence constraints.
J. Complexity 6, No.2, 149-165, 1990.
E. A. Galperin and N. M. Yanev.
One-dimensional analogue of the global optimization.
C. R. Acad. Bulgare Sci., 53(5):29-32, 2000.
Wolf Gamerith.
Ueber gemischte unscharfe Ansaetze zum multi-criteria-Problem.
Methods Oper. Res. 43, 63-71, 1981.
R.V. Gamkrelidze, editor.
Progress in mathematics. Vol. 11: Probability theory,
mathematical statistics, and theoretical cybernetics. Translated from Russian
by J. S. Wood.
New York-London: Plenum Press., 1971.
M.S. Gamze.
A method for the parametrization of one-step problems of operative
linear stochastic programming.
Kibernetika (Kiev), 1:71-74, 79, 135, 1988.
Cheng Yun Gao.
Efficient equilibria and the core in a stochastic market.
Qufu Shifan Daxue Xuebao Ziran Kexue Ban, 21(5):119-122, 1995.
Linchun Gao and András Prékopa.
Lower and upper bounds for the probability that at least r and
exactly r out of n events occur.
Math. Inequal. Appl., 5(2):315-333, 2002.
Alfredo Garcia and Robert L. Smith.
Solving nonstationary infinite horizon stochastic production planning
problems.
Oper. Res. Lett., 27(3):135-141, 2000.
I. Garcia and G.T. Herman.
Global optimization by parallel constrained biased random search.
In State of the art in global optimization (Princeton, NJ,
1995), volume 7 of Nonconvex Optim. Appl., pages 433-455. Kluwer
Acad. Publ., Dordrecht, 1996.
J.-M. Garcia and A. Turgeon.
Gestion optimale d'un ensemble hydro-electrique: Aspects long terme
et court terme.
RAIRO, Autom. Syst. Anal. Control 15, 243-262, 1981.
M. a Pilar García-Carrasco Aponte.
Some cases of optimal allocation of observations.
Trabajos Estadíst. Investigación Oper., 30(2):3-31,
1979.
D. T. Gardner.
Flexibility in electric power planning: Coping with demand
uncertainty.
Energy, 21(2):1207-1218, 1996.
D. T. Gardner and J. S. Rogers.
Planning electric power systems under demand uncertainty with
different technology lead times.
Management Science, 45:1289-1306, 1999.
L.E. Garey and R.D. Gupta.
A note on continuous search algorithms.
J. Appl. Probab., 24(1):277-280, 1987.
N.I. Garkusha.
Investigation of the adaptive properties of production-transportation
systems by means of stochastic models.
Issled. Operatsii i ASU, 35:11-20, 1991.
Izaskun Garrido and Marc C. Steinbach.
A multistage stochastic programming approach in real-time process
control.
In Online optimization of large scale systems, pages 479-498.
Springer, Berlin, 2001.
Stanley J. Garstka.
Stochastic programs with recourse: random recourse costs only.
Management Sci., 19:747-750, 1972/73.
Stanley J. Garstka.
Regularity conditions for a class of convex programs.
Management Sci., Theory 20, 373-377, 1973.
Stanley J. Garstka.
Distribution functions in stochastic programs with recourse: a
parametric analysis.
Math. Programming, 6:339-351, 1974.
Stanley J. Garstka.
On duality in stochastic programming with recourse.
Z. Wahrscheinlichkeitstheorie und Verw. Gebiete, 29:21-24,
1974.
Stanley J. Garstka.
The economic equivalence of several stochastic programming models.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 83-91, London, 1980. Academic Press.
Stanley J. Garstka.
An economic interpretation of stochastic programs.
Math. Programming, 18(1):62-67, 1980.
Stanley J. Garstka and David P. Rutenberg.
Computation in discrete stochastic programs with recourse.
Operations Res., 21:112-122, 1973.
Mathematical programming and its applications.
Stanley J. Garstka and Roger J.-B. Wets.
On decision rules in stochastic programming.
Math. Programming, 7:117-143, 1974.
Bernd Gärtner and Emo Welzl.
Linear programming-randomization and abstract frameworks.
In STACS 96 (Grenoble, 1996), volume 1046 of Lecture Notes
in Comput. Sci., pages 669-687, Berlin, 1996. Springer.
H. Gassmann.
Conditional probability and conditional expectation of a random
vector.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 237-254. Springer, Berlin, 1988.
H. Gassmann.
MSLIP, a computer code for the multistage stochastic linear
programming problem.
Mathematical Programming, 47:407-423, 1990.
H. Gassmann and W.T. Ziemba.
A tight upper bound for the expectation of a convex function of a
multivariate random variable.
Math. Programming Stud., 27:39-53, 1986.
Stochastic programming 84. I.
H. I. Gassmann and András Prékopa.
On stages and consistency checks in stochastic programming.
Oper. Res. Lett., 33(2):171-175, 2005.
H. I. Gassmann and E. Schweitzer.
A comprehensive input format for stochastic linear programs.
Ann. Oper Res., 104:89-125 (2002), 2001.
Modeling languages and systems.
H.I. Gassmann and A.M. Ireland.
On the formulation of stochastic linear programs using algebraic
modelling languages.
Ann. Oper. Res., 64:83-112, 1996.
Stochastic programming, algorithms and models (Lillehammer, 1994).
H.I. Gassmann and J.D. Pinter.
Modelling tools for stochastic programs.
Working Paper 97-1, School of Business Administration, Dalhousie
University, Halifax, 1997.
Horand I. Gassmann.
The SMPS format for stochastic linear programs.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 9-19. SIAM, Philadelphia, PA, 2005.
Horand I. Gassmann and David M. Gay.
An integrated modeling environment for stochastic programming.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 159-175. SIAM, Philadelphia, PA, 2005.
Horand I. Gassmann, Sandra L. Schwartz, Stein W. Wallace, and William T.
Ziemba.
Introduction to stochastic programming applications.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 179-184. SIAM, Philadelphia, PA, 2005.
Horand I. Gassmann and Stein W. Wallace.
Solving linear programs with multiple right-hand sides: pricing and
ordering schemes.
Ann. Oper. Res., 64:237-259, 1996.
Stochastic programming, algorithms and models (Lillehammer, 1994).
Horand I. Gassmann, Stein W. Wallace, and William T. Ziemba.
Stochastic programming computer implementations.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 3-8. SIAM, Philadelphia, PA, 2005.
W. Gaul.
On stochastic analysis of project-networks.
In Deterministic and stochastic scheduling, Proc. NATO Adv.
Study Res. Inst., Durham/Engl. 1981, 297-309, 1982.
W. Gaul.
Financial planning via stochastic programming: a stochastic
flows-with-gains approach.
In Risk and capital (Ulm, 1983), volume 227 of Lecture
Notes in Econom. and Math. Systems, pages 181-197. Springer, Berlin, 1984.
Wolfgang Gaul.
Bounds for the expected duration of a stochastic project planning
model.
J. Inform. Optim. Sci., 2(1):45-63, 1981.
Wolfgang Gaul.
Stochastische Projektplanung und Marketingprobleme.
Methods Oper. Res. 44, 539-552, 1981.
Wolfgang Gaul.
Marketing-Logistik bei stochastischer Nachfrage.
In Operations research, Proc. 11th Annu. Meet., Frankfurt a.M.
1982, 73-80 , 1983.
Carlo Gavarini.
Applications de la programmation mathematique a l'analyse limite des
structures.
Revue Franc. Automat. Inform. Rech. operat. 7, V-3, 55-68,
1973.
T. Gavin.
Etudes numeriques et applications de processus d'approximation
stochastique.
Ann. sci. Univ. Clermont 58, Math. 12, 94-109, 1976.
Andrzej Gawrych-Zukowski.
Some problems of multi-objective programming with fuzzy
constraints.
Syst. Sci. 7, 75-86, 1981.
A.A. Gayvoronskiy and Yu. M. Yermol'yev.
Problems of stochastic programming with subsequent estimation of the
parameters.
Engrg. Cybernetics, 17(4):22-31 (1980), 1979.
H.P. Geering and M. Mansour(ed.).
Large scale systems; theory and applications 1986. Selected papers
from the 4th IFAC/IFORS Symposium, Zuerich, Switzerland, August 26-29, 1986.
Volume 2.
IFAC Proceedings Series, 1987, No.11. International Federation
of Automatic Control. Oxford etc.: Pergamon Press. XV, 396 p., ISBN 0-08-
034084-9, 1987.
S. Geetha and K.P.K. Nair.
A stochastic bottleneck transportation problem.
J. Oper. Res. Soc. 45, No.5, 583-588, 1994.
William V. Gehrlein and Peter C. Fishburn.
Information overload in mechanical processes.
Management Sci. 23, 391-398, 1976.
Robert Geist and Robert Reynolds.
The most likely steady state for large numbers of stochastic
traveling salesman.
In Numerical solution of Markov chains, volume 8 of
Probab. Pure Appl., pages 529-541. Dekker, New York, 1991.
Saul B. Gelfand and Sanjoy K. Mitter.
Recursive stochastic algorithms for global optimization in r\sp d.
SIAM J. Control Optim., 29(5):999-1018, 1991.
S.B. Gelfand and S.K. Mitter.
Weak convergence of Markov chain sampling methods and annealing
algorithms to diffusions.
J. Optim. Theory Appl., 68(3):483-498, 1991.
Mitsuo Gen and Baoding Liu.
Evolution algorithm for optimal capacity expansion.
J. Oper. Res. Soc. Japan, 40(1):1-9, 1997.
Ian P. Gent, Ewan MacIntyre, Patrick Prosser, Barbara M. Smith, and Toby Walsh.
Random constraint satisfaction: flaws and structure.
Constraints, 6(4):345-372, 2001.
Yigal Gerchak and Mordechai Henig.
An inventory model with component commonality.
Oper. Res. Lett. 5, 157-160, 1986.
L. Gerencser.
The application of a stochastic Broyden method in the self-tuning
control of a heatpump.
Z. Angew. Math. Mech. 63, No.5, T408-T410, 1983.
L. Gerencser.
Convergence of a stochastic variable metric method with application
in adaptive prediction.
In System modelling and optimization, Proc. 11th IFIP Conf.,
Copenhagen 1983, Lect. Notes Control Inf. Sci. 59, 443-450, 1984.
L. Gerencsér.
Strong consistency theorems related to stochastic quasi-Newton
methods.
In Stochastic optimization (Kiev, 1984), volume 81 of
Lecture Notes in Control and Inform. Sci., pages 364-372. Springer, Berlin,
1986.
L. Gerencsér.
Rate of convergence of moments of Spall's SPSA method.
In Stochastic Differential and Difference Equations, Series on
Progress in Systems and Control Theory, volume 23, pages 67-75.
Birkhäuser, Boston, 1997.
L. Gerencsér.
Convergence rate of moments in stochastic approximation with
simultaneous perturbation gradient approximation and resetting.
IEEE Transactions on Automatic Control, 44:894-905, 1999.
L. Gerencsér, S.D. Hill, and Z. Vágó.
Optimization over discrete sets using SPSA.
In Proceedings of the IEEE Conference on Decision and
Control, pages 1791-1795, 1999.
L. Gerencsér and Z. Vágó.
SPSA in noise-free optimization.
In Proceedings of the American Control Conference, pages
3284-3288, 2000.
László Gerencsér and Zsuzsanna Vágó.
Non-smooth optimization with randomization.
In Optimization theory (Mátraháza, 1999), volume 59 of
Appl. Optim., pages 111-117. Kluwer Acad. Publ., Dordrecht, 2001.
László Gerencsér, Zsuzsanna Vágó, and H. Hjalmarsson.
Randomization methods in optimization and adaptive control.
In Stochastic theory and control (Lawrence, KS, 2001), volume
280 of Lecture Notes in Control and Inform. Sci., pages 137-153.
Springer, Berlin, 2002.
Allen Gersho.
Adaptive filtering with binary reinforcement.
IEEE Trans. Inf. Theory IT-30, 191-199, 1984.
Dieter Geuting and Klaus Hellwig.
Einige Bemerkungen zum Zweistufigen Stochastischen Programmieren.
In Operations Research Verfahren XXIV, Symp. Mannheim 1978,
57-59, 1977.
Horst-Dieter Geuting.
Entscheidungen unter Ungewissheit. Loesungsansaetze der
stochastischen Programmierung bei endlichem Zustandsraum., 1978.
Charles J. Geyer.
On the asymptotics of constrained M-estimation.
Ann. Statist., 22(4):1993-2010, 1994.
G.Gürkan.
Simulation optimization of buffer allocations in production lines
with unreliable machines.
Annals of Operations Research, 93:177-216, 2000.
G.Gürkan, A.Y. Özge, and S.M. Robinson.
Sample-path solutions for simulation optimization problems and
stochastic variational inequalities.
In D.L. Woodruff, editor, Advances in Computational and
Stochastic Optimization, Logic Programming, and Heuristic Search: Interfaces
in Computer Science and Operations Research, pages 169-188, Boston, 1998.
Kluwer Academic Publishers.
G.Gürkan, A.Y. Özge, and S.M. Robinson.
Solving stochastic optimization problems with stochastic constraints:
An application in network design.
In P.A. Farrington, H.B. Nembhard, D.T. Sturrock, and G.W. Evans,
editors, Proceedings of the 1999 Winter Simulation Conference, pages
471-478, 1999.
Saied Ghannadan and Stein W. Wallace.
Feasibility in uncapacitated networks: the effect of individual arcs
and nodes.
Ann. Oper. Res., 64:197-210, 1996.
Stochastic programming, algorithms and models (Lillehammer, 1994).
G. Giappichelli, editor.
Atti del Primo Convegno A.M.A.S.E.S., Pisa, November 4-5,
1977., 1979.
Torino: VIII, 393 p. L 10.000.00.
Basilis Gidas.
Simulations and global optimization.
In Random media, IMA Vol. Math. Appl. 7, 129-145, 1987.
A.B. Gil', A.I. Kaplinskii, and V. M. Umyvakin.
Information base of procedures for searching for optimal variants of
an object that does not use differential characteristics of the quality
exponent.
In Optimization and modeling in automated systems (Russian),
pages 4-10. Voronezh. Politekhn. Inst., Voronezh, 1988.
È. Kh. Gimadi.
Justification of a priori estimates for the quality of the
approximate solution of a standardization problem.
Upravlyaemye Sistemy, 27:12-27, 88-89, 1987.
M.Ja. Ginzburg and M.I. Burtman.
Ueber die operative Ressourcenverteilung auf grossen zweiteiligen
Transportnetzen ohne Zyklen.
Ekonom. i Mat. Metody 13, 302-310, 1977.
V.L. G¯irko.
Limit theorems for the solutions of systems of linear random
equations and the eigenvalues and determinants of random matrices.
Dokl. Akad. Nauk SSSR, 212:1039-1042, 1973.
V.L. Girko and V.V. Smirnova.
Asimptoticheskie metody resheniya nekotorykh zadach linei
nogo stokhasticheskogo programmirovaniya i modelei tipa zatraty-vypusk,
volume 83 of Preprint.
Akad. Nauk Ukrain. SSR Inst. Kibernet., Kiev, 1983.
V.L. Girko and V.V. Smirnova.
Method of integral representations for the solution of problems of
linear stochastic programming.
Kibernetika 1983, No.6, 122-124, 1983.
Vyacheslav L. Girko.
Integral representation and resolvent methods for solving of linear
stochastic programming problems of large dimensions.
In System modelling and optimization (Zurich, 1991), volume 180
of Lecture Notes in Control and Inform. Sci., pages 574-579. Springer,
Berlin, 1992.
M.B. Gitman, P.V. Trusov, and S.A. Fedoseev.
On the stochastic optimization problems of plastic metal-working
processes.
J. Math. Sci. (New York), 84(3):1109-1112, 1997.
Michael B. Gitman, Peter V. Trusov, and Sergei A. Fedoseev.
On the stochastic optimization problems of plastic metal working
processes under stochastic initial conditions.
Korean J. Comput. Appl. Math., 6(1):111-125, 1999.
J.C. Gittins.
Resource allocation in speculative chemical research.
J. appl. Probab. 11, 255-265, 1974.
K.D. Glazebrook.
On non-preemptive strategies for stochastic scheduling problems in
continuous time.
Int. J. Syst. Sci. 12, 771-782, 1981.
K.D. Glazebrook.
On a sufficient condition for superprocesses due to Whittle.
J. Appl. Probab. 19, 99-110, 1982.
K.D. Glazebrook, R.J. Boys, and N.A. Fay.
On the evaluation of strategies for branching bandit processes.
Ann. Oper. Res. 30, 299-319, 1991.
Kevin D. Glazebrook.
Semi-Markov models for single-machine stochastic scheduling
problems.
Int. J. Syst. Sci. 16, 573-587, 1985.
Alan Gleit.
Statistical decision theory and linear programming. The
non-constrained case., 1977.
Alan Gleit.
Stochastic linear programming.
Matematisk Institut, Aarhus Universitet, Aarhus, 1977.
Lecture Notes Series, No. 49.
I.A. Glinkin.
On the search for global extrema of functions which are
representable in the form of an integral on a domain of variable
configuration.
Vopr. Kibern., Mosk. 122, 91-97, 1985.
I.A. Glinkin.
Search for the global extremum of functions represented as an
integral over a domain of variable configuration.
Voprosy Kibernet. (Moscow), 122:91-97, 1985.
B.M. Glover, B.D. Craven, and S.D. Flam.
A generalized Karush-Kuhn-Tucker optimality condition without
constraint qualification using the approximate subdifferential.
Numer. Funct. Anal. Optimization 14, No.3-4, 333-353, 1993.
V.M. Gluskov and G.B. Olejars.
Einige Probleme der stochastischen Programmierung, die in
Dialogsystemen der Planung unter den Verhaeltnissen unvollstaendiger
Informationen entstehen.
Kibernetika, Kiev 1977, No.3, 100-104, 1977.
O.V. Gluskova.
Finite-difference methods for solving stochastic minimax problems.
Dokl. Akad. Nauk Ukrain. SSR Ser. A, 10:78-81, 96, 1980.
V.A. Gnatjuk.
An algorithm for the simultaneous solution of the direct and the dual
problems of convex programming in a Banach space.
Kibernetika (Kiev), 1:102-107, 1975.
Willy Gochet.
A note on the E-model of chance-constrained programming with random
b- vector.
Management Sci., Theory 21, 844-845, 1975.
Willy F. Gochet and Manfred W. Padberg.
The triangular E-model of chance-constrained programming with
stochastic A-matrix.
Management Sci., 20:1284-1291, 1973/74.
A.F. Godonoga.
Some properties in problems of quadratic stochastic programming.
Mat. Issled., 82:24-29, 152, 1985.
Mat. Modeli Met. Optim.
A.F. Godonoga.
Stochastic schemes for finding the minimax.
Mat. Issled., 116:3-13, 96, 1990.
Veroyatnost. i Prilozhen.
C.D. Godsil.
Tools from linear algebra.
In Handbook of combinatorics, Vol. 1, 2, pages 1705-1748.
Elsevier, Amsterdam, 1995.
With an appendix by L. Lovász.
Vikas Goel and Ignacio E. Grossmann.
A class of stochastic programs with decision dependent uncertainty.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Vikas Goel and Ignacio E. Grossmann.
A class of stochastic programs with decision dependent uncertainty.
Math. Program., 108(2-3, Ser. B):355-394, 2006.
A.S. Gofmark.
Stochastic programming problems with penalty estimates when the
coefficients of the matrix A are random.
Taskent. Inst. Narod. Hoz. Naucn. Zap., 34:68-76, 1970.
Mat. v Prilozen.
A.S. Gofmark.
Stochastic programming problems with probabilistic constraints when
the coefficients of the matrix A are random.
Taskent. Inst. Narod. Hoz. Naucn. Zap., 34:77-87, 1970.
Mat. v Prilozen.
C.J. Goh and K.L. Teo.
On constrained stochastic optimal parameter selection problems.
Bull. Austral. Math. Soc., 41(3):393-405, 1990.
Ambrose Goicoechea and Lucien Duckstein.
Nonnormal deterministic equivalents and a transformation in
stochastic mathematical programming.
Appl. Math. Comput., 21(1):51-72, 1987.
K. Gokbayrak and C. G. Cassandras.
Online surrogate problem methodology for stochastic discrete resource
allocation problems.
J. Optim. Theory Appl., 108(2):349-376, 2001.
K. Gokbayrak and C. G. Cassandras.
Generalized surrogate problem methodology for online stochastic
discrete optimization.
J. Optim. Theory Appl., 114(1):97-132, 2002.
Bruce L. Golden, Arjang A. Assad, and Stelios H. Zanakis.
Statistics and optimization: the interface.
Amer. J. Math. Management Sci., 4(1-2):1-5, 1984.
Statistics and optimization : the interface.
Thomas Goll and Jan Kallsen.
Optimal portfolios for logarithmic utility.
Stochastic Process. Appl., 89(1):31-48, 2000.
R. Gollmer, M. P. Nowak, W. Römisch, and R. Schultz.
Unit commitment in power generation-a basic model and some
extensions.
Annals of Operations Research, 96:167-189, 2000.
Ralf Gollmer, Uwe Gotzes, and Rüdiger Schultz.
Second-order stochastic dominance constraints induced by
mixed-integer linear recourse.
Stochastic Programming E-Print Series, http://www.speps.org,
2007.
Ralf Gollmer, Frederike Neise, and Rüdiger Schultz.
Stochastic programs with first-order dominance constraints induced by
mixed-integer linear recourse.
Stochastic Programming E-Print Series, http://www.speps.org,
2007.
Alexandr N. Golodnikov, Pavel S. Knopov, Panos M. Pardalos, and Stanislav P.
Uryasev.
Optimization in the space of distribution functions and applications
in the Bayes analysis.
In Probabilistic constrained optimization, volume 49 of
Nonconvex Optim. Appl., pages 102-131. Kluwer Acad. Publ., Dordrecht, 2000.
A.N. Golodnikov.
Search for the extremum of a linear functional in a class of
distribution functions satisfying linear constraints of the inequality type.
Akad. Nauk Ukrain. SSR Inst. Kibernet. Preprint, 13:7-14, 53,
1979.
Stochastic optimization models.
A.N. Golodnikov.
A method of solving certain stochastic problems with partially
defined distribution function.
Engrg. Cybernetics, 18(2):14-20 (1981), 1980.
A.N. Golodnikov, Yu.M. Ermol'ev, and Ts.Kh. Nedeva.
Problems of stochastic programming with unknown distribution
functions.
Izv. Akad. Nauk SSSR, Tekh. Kibern. 1983, No.3, 180-188, 1983.
B. Golub, M. Holmer, R. McKendall, L. Pohlman, and S.A. Zenios.
Stochastic programming models for money management.
European Journal of Operations Research, 85:282-296, 1995.
Luiz F.A.M. Gomes.
On modelling equilibrium traffic flow assignment with elastic demand
as a stochastic nonlinear vector optimization problem.
Found. Control Eng. 11, 157-166, 1986.
Amilcar S. Goncalves.
On the convexity of sets of parameters in parametric linear
programming.
Univ. Lisboa, Revista Fac. Ci., II. Ser., A 14, 61-78, 1972.
J. Gondzio and A. Ruszczy\'nski.
Sensitivity method for basis inverse representation in multistage
stochastic linear programming problems.
J. Optim. Theory Appl., 74(2):221-242, 1992.
Jacek Gondzio.
Simplex modifications exploiting special features of dynamic and
stochastic dynamic linear programming problems.
Control Cybernet., 17(4):337-349 (1989), 1988.
Jacek Gondzio and Roy Kouwenberg.
High performance computing for asset liability management.
Report MS-99-004, Econometric Institute, Erasmus University
Rotterdam, The Netherlands, 1999.
Available at http://mail.tinbinst.nl/~kouwenbe/ms99004.ps.
Jacek Gondzio and Andrzej Ruszczy\'nski.
A sensitivity method for solving multistage stochastic linear
programming problems.
In Aspiration based decision support systems, volume 331 of
Lecture Notes in Econom. and Math. Systems, pages 68-79. Springer,
Berlin, 1989.
Linguo Gong.
Chance-constrained linear programming with location scale
distributions.
Nav. Res. Logist. 39, No.7, 997-1007, 1992.
Wei-Bo Gong, Yu-Chi Ho, and Wengang Zhai.
Stochastic comparison algorithm for discrete optimization with
estimation.
SIAM J. Optim., 10(2):384-404 (electronic), 2000.
Graham C. Goodwin, David J. Hill, and Xie Xianya.
Stochastic adaptive control for exponentially convergent
time-varying systems.
SIAM J. Control Optimization 24, 589-603, 1986.
V.M. Gorbachuk.
Stability of solutions of stochastic extremal problems.
In Mathematical methods for the analysis of complex stochastic
systems (Russian), pages 71-76, vi. Akad. Nauk Ukrain. SSR Inst. Kibernet.,
Kiev, 1988.
Steven M. Gorelick.
Large scale nonlinear deterministic and stochastic optimization:
Formulations involving simulation of subsurface contamination.
Math. Program., Ser. B 48, No.1, 19-39, 1990.
B. G. Gorenstin, N. M. Campodónico, J. P. Costa, and M. V. F. Pereira.
Stochastic optimization of a hydrothermal system including network
constraints.
IEEE Transactions on Power Systems, 7(2):791-797, 1992.
B. G. Gorenstin, N. M. Campodónico, J. P. Costa, M. V. F. Pereira, and
N. Deeb.
Power-system expansion planning under uncertainty.
IEEE Transactions on Power Systems, 8(1):129-136, 1993.
A. Gorka and M. Kostreva.
Probabilistic version of the method of feasible directions.
Appl. Math. Comput., 130(2-3):253-264, 2002.
S. Yu. Gorodetskii.
Convergence and asymptotic estimates of behavior for a class of
search methods.
Dinamika Sistem, Dinam. Uprav.:140-163, 195, 1984.
S.Yu. Gorodetskij and Yu.I. Nejmark.
On the search characteristics of the algorithm of global
optimization with an adaptive stochastic model.
Probl. Sluchajnogo Poiska 9, 83-105, 1981.
Uwe Gotzes.
Optimal investments in distributed generation units under
uncertainty.
In CTW2006-Cologne-Twente Workshop on Graphs and Combinatorial
Optimization, volume 25 of Electron. Notes Discrete Math., page 65
(electronic). Elsevier, Amsterdam, 2006.
A. Gouda, D. Monhor, and T. Szántai.
Stochastic programming based PERT modeling.
In Coping with uncertainty, volume 581 of Lecture Notes in
Econom. and Math. Systems, pages 241-255. Springer, Berlin, 2006.
A. Gouda and T. Szántai.
A new, stochastic programming model of PERT.
Alkalmaz. Mat. Lapok, 22(1):97-113, 2005.
John Goutsias and Jerry M. Mendel.
Optimal simultaneous detection and estimation of filtered discrete
semi- Markov chains.
IEEE Trans. Inf. Theory IT-34, No.3, 551-568, 1988.
A.K. Govil and S. Kumar.
Stochastic behaviour of a complex system under priority repair.
Computing 6, 200-213, 1970.
Alicja Grabowska.
Randomness of a restriction vector in linear programming.
Przeglk ad Statyst., 18:345-352, 1971.
O.N. Granichin.
Estimation of the minimum point of an unknown function observed
against the background of dependent noise.
Problemy Peredachi Informatsii, 28(2):16-20, 1992.
V.I. Gricenko and N.A. Nazarenko.
Problem of on-line control of multiphase supply systems.
Kibernetika, Kiev 1978, No.2, 100-103, 1978.
M.J. Grimble.
Convergence of explicit LQG self-tuning controllers.
IEE Proc., Part D 135, No.4, 309-322, 1988.
S.N. Grincenko and L.A. Rastrigin.
On matrix random search.
Avtomat. vycislit. Tehn., Riga 1977, No.1, 48-51, 1977.
Richard C. Grinold.
A new approach to multistage stochastic linear programs.
Math. Programming Stud., 6:19-29, 1976.
Stochastic systems: modeling, identification and optimization, II
(Proc. Sympos., Univ. Kentucky, Lexington, Ky., 1975).
Richard C. Grinold.
Infinite horizon stochastic programs.
SIAM J. Control Optim., 24(6):1246-1260, 1986.
P. J. F. Groenen, W. J. Heiser, and J. J. Meulman.
Global optimization in least-squares multidimensional scaling by
distance smoothing.
J. Classification, 16(2):225-254, 1999.
Luuk Groenewegen and Jaap Wessels.
Conditions for optimality in multistage stochastic programming
problems.
In Recent results in stochastic programming (Proc. Meeting,
Oberwolfach, 1979), volume 179 of Lecture Notes in Econom. and Math.
Systems, pages 41-57. Springer, Berlin, 1980.
Nicole Groewe and Werner Roemisch.
A stochastic programming model for optimal power dispatch: Stability
and numerical treatment.
In Stochastic optimization. Numerical methods and technical
applications, Proc. GAMM/IFIP-Workshop, Neubiberg/Ger. 1990, Lect. Notes
Econ. Math. Syst. 379, 111-139, 1992.
M.A. Grove.
A surrogate for linear programs with random requirements.
Eur. J. Oper. Res. 34, No.3, 399-402, 1988.
Nicole Gröwe, Werner Römisch, and Rüdiger Schultz.
A simple recourse model for power dispatch under uncertain demand.
Ann. Oper. Res., 59:135-164, 1995.
Models for planning under uncertainty.
N. Gröwe-Kuska, K. C. Kiwiel, M. P. Nowak, W. Römisch, and I. Wegner.
Power management in a hydro-thermal system under uncertainty by
Lagrangian relaxation.
In C. Greengard and A. Ruszczy\'nski, editors, Decision Making
under Uncertainty: Energy and Power, volume 128 of IMA volumes on
Mathematics and its Applications, pages 39-70. Springer-Verlag, 2002.
N. Gröwe-Kuska, K.C. Kiwiel, M.P. Nowak, W. Römisch, and I. Wegner.
Power management under uncertainty by lagrangian relaxation.
In Proceedings of the 6th International Conference Probabilistic
Methods Applied to Power Systems (PMAPS 2000), volume 2, INESC Porto, 2000.
Viviane Grunert da Fonseca, Carlos M. Fonseca, and Andreia O. Hall.
Inferential performance assessment of stochastic optimisers and the
attainment function.
In Evolutionary multi-criterion optimization (Zurich, 2001),
volume 1993 of Lecture Notes in Comput. Sci., pages 213-225. Springer,
Berlin, 2001.
Fu Wen Gu.
Density estimation of measurable optimal value functions and
measurable solutions in stochastic programming.
J. Chengdu Univ. Sci. Tech., 2:143-146, 1987.
Fu Wen Gu.
Existence of optimal solutions for probabilistic constrained linear
programming problems.
J. Math. Res. Exposition, 15(2):293-296, 1995.
Fu Wen Gu.
An approximation method for a class of constrained probabilistic
programs and its convergence.
Sichuan Daxue Xuebao, 34(4):399-405, 1997.
Fuwen Gu.
Estimating the density function of optimal value function and
measurable solution of stochastic programming.
J. Chengdu Univ. Sci. Technol. 1987, No.2, 143-146, 1987.
Jun Gu.
Randomized and deterministic local search for SAT and
scheduling problems.
In Randomization methods in algorithm design (Princeton, NJ,
1997), pages 61-108. Amer. Math. Soc., Providence, RI, 1999.
S. Guan and S.-C. Fang.
Linear programming with stochastic elements: an on-line approach.
Comput. Math. Appl., 33(9):61-82, 1997.
Sichong Guan and Shu-Cherng Fang.
A global-filtering algorithm for linear programming problems with
stochastic elements.
Math. Methods Oper. Res., 48(3):287-316, 1998.
Yongpei Guan, Shabbir Ahmed, and George L. Nemhauser.
A branch-and-cut algorithm for the stochastic uncapacitated
lot-sizing problem.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Yongpei Guan, Shabbir Ahmed, and George L. Nemhauser.
Cutting planes for multi-stage stochastic integer programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
Yongpei Guan, Shabbir Ahmed, George L. Nemhauser, and Andrew J. Miller.
A branch-and-cut algorithm for the stochastic uncapacitated
lot-sizing problem.
Math. Program., 105(1, Ser. A):55-84, 2006.
Yongpei Guan and Andrew Miller.
Polynomial time algorithms for stochastic uncapacitated lot-sizing
problems.
Optimization Online, http://www.optimization-online.org, 2006.
J. Guddat, W. Roemisch, and R. Schultz.
Some applications of mathematical programming techniques in optimal
power dispatch.
Computing 49, No.3, 193-200, 1992.
Juergen Guddat, Francisco Guerra Vasquez, Klaus Tammer, and Klaus Wendler.
Multiobjective and stochastic optimization based on parametric
optimization.
Mathematical Research, 26. Berlin: Akademie-Verlag., 1985.
Herbert Guelicher.
Nichtlineares Programmieren unter stochastischen Nebenbedingungen.
Eine Untersuchung im Verkehrssektor., 1964.
Franco Guerriero and John Miltenburg.
The stochastic U-line balancing problem.
Naval Res. Logist., 50(1):31-57, 2003.
Roger Guesnerie.
Sur l'usage de la notion de probabilités subjectives.
Rev. Française Automat. Informat. Recherche
Opérationnelle, 6(Ser. V-1):31-46, 1972.
Harvey S. Gunderson, James G. Morris, and Howard E. Thompson.
Stochastic programming with recourse: A modification from an
applications viewpoint.
J. Oper. Res. Soc. 29, 769-778, 1978.
Xin Guo and Larry Shepp.
Option pricing in a world with arbitrage.
In Stochastic optimization: algorithms and applications
(Gainesville, FL, 2000), volume 54 of Appl. Optim., pages 87-96.
Kluwer Acad. Publ., Dordrecht, 2001.
A.M. Gupal.
A certain stochastic programming problem with constraints of a
probabilistic nature.
Kibernetika (Kiev), 6:94-100, 1974.
A.M. Gupal.
Eine Optimierungsmethode mit nichtstationaeren Restriktionen.
Kibernetika, Kiev 1974, Nr. 3, 131-133, 1974.
A.M. Gupal.
A method of optimization under nonstationary conditions.
Kibernetika (Kiev), 3:131-133, 1974.
A.M. Gupal.
Ueber ein Problem der stochastischen Programmierung mit
Beschraenkungen von wahrscheinlichkeitstheoretischer Natur.
Kibernetika, Kiev 1974, Nr. 6, 94-100, 1974.
A.M. Gupal.
A method for the solution of stochastic programming problems.
In Methods of operations research and reliability theory in
systems analysis (Russian), pages 97-105, Kiev, 1976. Akad. Nauk Ukrain.
SSR Inst. Kibernet.
A.M. Gupal.
Direct method of solution of stochastic programming problems.
Automat. Remote Control, 38(4, part 1):502-506, 1977.
A.M. Gupal.
Algorithms for the random search of an extremum of
non-differentiable functions.
Probl. Sluchajnogo Poiska 6, 121-129, 1978.
A.M. Gupal.
A method of solving stochastic problems with constraints of a
probabilistic nature.
Engrg. Cybernetics, 17(1):26-31 (1980), 1979.
A.M. Gupal.
Stochastic Methods for Solving Nonsmooth Extremal Problems.
Naukova Dumka, Kiev, 1979.
(in Russian).
A.M. Gupal.
Stochastic methods of minimization of nondifferentiable functions.
Autom. Remote Control 40, 529-534 translation from Avtom.
Telemekh. 1979, No.4, 61-66 (1979)., 1979.
A.M. Gupal and L.G. Bazenov.
A stochastic analogue of the conjugate gradient method.
Kibernetika (Kiev), 1:125-126, 1972.
A.M. Gupal and L.G. Bazenov.
A stochastic linearization method.
Kibernetika (Kiev), 3:116-117, 1972.
A.M. Gupal and V.B. Dubrovskii.
Finite-difference Arrow-Hurwicz method with averaging.
Kibernetika (Kiev), 4:iv, 120-122, 136, 1985.
A.M. Gupal and F. Mirzoahmedov.
A way of regulating the step in stochastic programming methods.
Kibernetika (Kiev), 1:133-134, 1978.
Anupam Gupta, Martin Pál, Ramamoorthi Ravi, and Amitabh Sinha.
What about Wednesday? Approximation algorithms for multistage
stochastic optimization.
In Approximation, randomization and combinatorial optimization,
volume 3624 of Lecture Notes in Comput. Sci., pages 86-98. Springer,
Berlin, 2005.
Anupam Gupta, R. Ravi, and Amitabh Sinha.
LP rounding approximation algorithms for stochastic network design.
Math. Oper. Res., 32(2):345-364, 2007.
N.K. Gupta.
Direct search computational methods for maximum likelihood parameter
estimation.
In Proc. 1978 IEEE Conf. on decision and control, incl. 17th
Symp. on adaptive processes, San Diego/Calif. 1979, 921-922, 1979.
P.K. Gupta and Man Mohan.
Linear programming and theory of games. 3rd extensively rev.
ed.
Tracts in Operations Research. New Delhi: Sultan Chand & Sons,
Publishers., 1981.
P.K. Gupta and Rakesh Kumar Verma.
Optimal resource allocation in a complex queueing system.
Math. Operationsforsch. Stat., Ser. Optimization 11, 507-512,
1980.
Sandipan Gupta and M. Chakraborty.
Stochastic fuzzy mathematical programming and decision.
J. Fuzzy Math., 6(3):637-647, 1998.
S.N. Gupta and A.K. Jain.
Optimization with the ratio of independent normal variates.
Acta Cienc. Indica Math., 12(3):209-212, 1986.
S.N. Gupta, A.K. Jain, and Kanti Swarup.
Stochastic linear fractional programming with the ratio of
independent Cauchy variates.
Naval Res. Logist., 34(2):293-305, 1987.
S.N. Gupta and Rajeev Kumar Jain.
Stochastic fractional programming under chance constraints with
random technology matrix.
Acta Cienc. Indica Math., 12(3):191-198, 1986.
S.N. Gupta and Kanti Swarup.
Note on stochastic programming for minimization of variance.
New Zealand Oper. Res., 8(2):185-187, 1980.
S.N. Gupta, Kanti Swarup, and Banwarilal.
Stochastic fractional programming under chance constraints with
random technology matrix.
Gujarat Statist. Rev., 8(1):23-34, 1981.
G. Gürkan.
Performance Optimization in Simulation: Sample-Path Optimization
of Buffer Allocations in Tandem Lines.
Ph.D. Dissertation, Department of Industrial Engineering,
University of Wisconsin-Madison, Madison, WI, 1996.
Gül Gürkan, A. Yonca Özge, and Stephen M. Robinson.
Sample-path solution of stochastic variational inequalities.
Math. Program., 84(2, Ser. A):313-333, 1999.
O.V. Guseva.
On convergence of the random search method.
Kibernetika, Kiev 1971, No.6, 143-145, 1971.
O.V. Guseva.
Random minimization without computation of derivatives.
Kibernetika (Kiev), 1:117-120, 1974.
Walter J. Gutjahr and Georg Ch. Pflug.
Simulated annealing for noisy cost functions.
J. Global Optim., 8(1):1-13, 1996.
Knut Haase.
Lotsizing and scheduling for production planning.
Lecture Notes in Economics and Mathematical Systems 408.
Springer-Verlag, 1994.
K. Hagglof.
The implementation of the stochastic branch and bound method for
applications in river basin water quality management.
IIASA Working Paper WP-96-89, Laxenburg, Austria, 1996.
A. Hahnewald-Busch and V. Nollau.
An approximation procedure for stochastic dynamic programming in
countable state space.
Math. Operationsforsch. Stat., Ser. Optimization 9, 109-117,
1978.
Bruce Hajek.
Optimization by simulated annealing: a necessary and sufficient
condition for convergence.
In Adaptive statistical procedures and related topics (Upton,
N.Y., 1985), volume 8 of IMS Lecture Notes-Monograph Ser., pages
417-427. Inst. Math. Statist., Hayward, Calif., 1986.
Bruce Hajek.
Cooling schedules for optimal annealing.
Math. Oper. Res. 13, No.2, 311-329, 1988.
Sylvia Halasz.
On a stochastic programming problem with random coefficients.
In Prog. Oper. Res., Eger 1974, Colloq. Math. Soc. Janos Bolyai
12, 493-498 , 1976.
Keshava P. Halemane and Ignacio E. Grossmann.
A remark on the paper: "Theoretical and computational aspects of
the optimal design centering, tolerancing, and tuning problem"\
[IEEE Trans. Circuits and Systems 26 (1979), no. 9,
795-813; MR 80h:94045] by E. Polak and A.
Sangiovanni-Vincentelli.
IEEE Trans. Circuits and Systems, 28(2):163-164, 1981.
S. Hamadene and J.-P. Lepeltier.
Zero-sum stochastic differential games and backward equations.
Systems Control Lett., 24(4):259-263, 1995.
Saïd Hamadene and Jean-Pierre Lepeltier.
Points d'équilibre dans les jeux stochastiques de somme non nulle.
C.R. Acad. Sci. Paris Sér. I Math., 318(3):251-256, 1994.
Alexandru Hampu.
Duality for multiobjective stochastic programming.
Gen. Math., 9(1-2):15-22, 2001.
Alexandru Hampu.
A stochastic programming model: the problem of minimum-risk with
simple recourse.
Int. J. Comput. Numer. Anal. Appl., 2(3):273-281, 2002.
Kum Sun Han and Ryong Sop Jang.
A theoretical consideration of stochastic improper linear
programming.
Su-hak, 4:21-23, 1994.
Gary Handler.
Termination policies for a two-state stochastic process.
Naval Res. Logist. Quart. 19, 281-292, 1972.
Pierre Hansen, Brigitte Jaumard, Marcus Poggi de Aragão, Fabien Chauny, and
Sylvain Perron.
Probabilistic satisfiability with imprecise probabilities.
Internat. J. Approx. Reason., 24(2-3):171-189, 2000.
Reasoning with imprecise probabilities (Ghent, 1999).
Behram Hansotia.
Some special cases of stochastic programs with recourse.
Operations Res., 25(2):361-363, 1977.
Behram J. Hansotia.
Stochastic linear programs with simple recourse: the equivalent
deterministic convex program for the normal, exponential, and Erlang cases.
Naval Res. Logist. Quart., 27(2):257-272, 1980.
J. Harband.
The existence of monotonic solutions of a nonlinear car-following
equation.
J. Math. Anal. Appl., 57(2):257-272, 1977.
Jr. Harris, Frederick C.
A stochastic optimization algorithm for Steiner minimal trees.
In Proceedings of the Twenty-fifth Southeastern International
Conference on Combinatorics, Graph Theory and Computing (Boca Raton, FL,
1994), volume 105, pages 54-64, 1994.
William E. Hart.
Sequential stopping rules for random optimization methods with
applications to multistart local search.
SIAM J. Optim., 9(1):270-290 (electronic), 1999.
Peter R. Hartley.
Value function approximation in the presence of uncertainty and
inequality constraints. An application to the demand for credit cards.
J. Econom. Dynam. Control, 20(1-3):63-92, 1996.
R. Hartley.
Inequalities in completely convex stochastic programming.
J. Math. Anal. Appl., 75(2):373-384, 1980.
Charles M. Harvey.
Stochastic programming models for decreasing risk aversion.
J. Oper. Res. Soc., 32(10):885-889, 1981.
Kurt Hässig.
Stochastische lineare Programmierung und ihre
Anwendungsmöglichkeiten in der Wirtschaft.
Hochschule St. Gallen, St. Gallen, 1971.
Dissertation der Hochschule St. Gallen für Wirtschafts- und
Sozialwissenschaften zur Erlangung der Würde eines Doktors der
Wirtschaftswissenschaften, Dissertation No. 399.
K. K. Haugen.
A stochastic dynamic programming model for scheduling of offshore
petroleum fields with resource uncertainty.
European Journal of Operational Research, 88:88-100, 1993.
Kjetil K. Haugen, Arne Løkketangen, and David L. Woodruff.
Progressive hedging as a meta-heuristic applied to stochastic
lot-sizing.
European J. Oper. Res., 132(1):116-122, 2001.
Kjetil K. Haugen and Stein W. Wallace.
Stochastic programming: potential hazards when random variables
reflect market interaction.
Ann. Oper. Res., 142:119-127, 2006.
D. Haugland, A. Hallefjord, and H. Asheim.
Models for petroleum field exploitation.
European Journal of Operational Research, 37(1):58-72, 1988.
Dag Haugland and Stein W. Wallace.
Solving many linear programs that differ only in the righthand side.
European J. Oper. Res., 37(3):318-324, 1988.
H. Hauptmann.
A statistic for judging solutions of the travelling salesman problem.
In IX. Oberwolfach Conference on Operations Research
(Oberwolfach, 1978), volume 36 of Operations Res. Verfahren, pages
61-71. Hain, Königstein/Ts., 1980.
A. Haurie, Y. Smeers, and G. Zaccour.
Toward a contract portfolio management model for a gas producing
firm.
INFOR, 30(3):257-273, 1992.
A. Haurie, G. Zaccour, and Y. Smeers.
Stochastic equilibrium programming for dynamic oligopolistic markets.
J. Optim. Theory Appl., 66(2):243-253, 1990.
Alain Haurie and Pierre L'Ecuyer.
Approximation and bounds in discrete event dynamic programming.
IEEE Trans. Autom. Control AC-31, 227-235, 1986.
Alain Haurie and Francesco Moresino.
Computation of S-adapted equilibria in piecewise deterministic
games via stochastic programming methods.
In Advances in dynamic games and applications (Maastricht,
1998), pages 225-252. Birkhäuser Boston, Boston, MA, 2001.
Kelly J. Hayhurst and Douglas R. Shier.
A factoring approach for the stochastic shortest path problem.
Oper. Res. Lett., 10(6):329-334, 1991.
Zhi Yong He and Chong Chao Huang.
Genetic algorithm based on simulation for two-stage stochastic
programming.
J. Math. (Wuhan), 24(6):690-694, 2004.
Bernd Heidergott.
A weak derivative approach to optimization of threshold parameters in
a multicomponent maintenance system.
J. Appl. Probab., 38(2):386-406, 2001.
T. Heikkinen and A. Prékopa.
Optimal power control in a wireless network using a model with
stochastic link coefficients.
Naval Res. Logist., 52(2):178-192, 2005.
W.-R. Heilmann.
Solving a dynamic program by linear programming - general state and
action spaces.
In Proc. Oper. Res. 7, Vortr. Jahrestag. DGOR, Kiel 1977,
31-38, 1978.
W.-R. Heilmann.
Solving stochastic dynamic programming problems by linear
programming-an annotated bibliography.
Z. Operations Res. Ser. A-B, 22(1):A43-A53, 1978.
Wolf-Rüdiger Heilmann.
Linear programming of dynamic programs with unbounded rewards.
In Operations Research Verfahren, XXIV, pages 94-105,
Meisenheim, 1977. Hain.
Wolf-Rüdiger Heilmann.
Optimal selectors for stochastic linear programs.
Appl. Math. Optim., 4(2):139-142, 1977/78.
Wolf-Rüdiger Heilmann.
Generalized linear programming of finite-stage dynamic programs.
In Second Symposium on Operations Research
(Rheinisch-Westfälische Tech. Hochsch. Aachen, Aachen, 1977), Teil 1,
Operations Res. Verfahren, XXVIII, pages 153-167. Hain, Königstein/Ts.,
1978.
Wolf-Rüdiger Heilmann.
A note on sequential minimax rules for stochastic linear programs.
In Recent results in stochastic programming (Proc. Meeting,
Oberwolfach, 1979), volume 179 of Lecture Notes in Econom. and Math.
Systems, pages 59-65. Springer, Berlin, 1980.
Wolf-Rüdiger Heilmann.
A note on chance-constrained programming.
J. Oper. Res. Soc., 34(6):533-537, 1983.
Wolf-Ruediger Heilmann.
Stochastische dynamische Optimierung als Spezialfall linearer
Optimierung in halbgeordneten Vektorraeumen.
Manuscr. Math. 23, 57-66, 1977.
Wolf-Ruediger Heilmann.
A linear programming approach to general non-stationary dynamic
programming problems.
Math. Operationsforsch. Stat., Ser. Optimization 10, 325-333,
1979.
Wolf-Ruediger Heilmann.
Solving multistage stochastic linear programming problems by non-
stationary dynamic programming.
Oper. Res. Verfahren 33, 173-184, 1979.
Wolf-Ruediger Heilmann.
Approximations and error bounds for multistage stochastic linear
programs. Part 2: Approximation by a "larger" model.
Methods Oper. Res. 37, 349-356, 1980.
Wolf-Ruediger Heilmann.
A mathematical programming approach to Strassen's theorem on
distributions with given marginals.
Math. Operationsforsch. Stat., Ser. Optimization 12, 593-596,
1981.
Stanislaw Heilpern.
Selected problems in the theory of fuzzy sets.
Mat. Stos. (3), 16:27-38 (1981), 1980.
Thomas Heinze.
An algorithm for multistage stochastic integer programs.
In CTW2006-Cologne-Twente Workshop on Graphs and Combinatorial
Optimization, volume 25 of Electron. Notes Discrete Math., page 69
(electronic). Elsevier, Amsterdam, 2006.
Thomas Heinze and Rüdiger Schultz.
A branch-and-bound method for multistage stochastic integer programs
with risk objectives.
Stochastic Programming E-Print Series, http://www.speps.org,
2007.
H. Heitsch, W. Römisch, and C. Strugarek.
Stability of multistage stochastic programs.
SIAM J. Optim., 17(2):511-525 (electronic), 2006.
Holger Heitsch and Werner Römisch.
Scenario tree modelling for multistage stochastic programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
Holger Heitsch, Werner Römisch, and Cyrille Strugarek.
Stability of multistage stochastic programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Michael Heitzer and Manfred Staat.
Limit and shakedown analysis with uncertain data.
In Stochastic optimization techniques (Neubiberg/Munich, 2000),
volume 513 of Lecture Notes in Econom. and Math. Systems, pages
253-267. Springer, Berlin, 2002.
Thorkell Helgason and Stein W. Wallace.
Approximate scenario solutions in the progressive hedging algorithm.
A numerical study with an application to fisheries management.
Ann. Oper. Res., 31(1-4):425-444, 1991.
Stochastic programming, Part II (Ann Arbor, MI, 1989).
G. Hellwig, P. Kall, and P. Abel, editors.
Statistical Methods for Decission Processes.
Daimler Benz AG, Stuttgart-Möhringen, 1994.
K. Hellwig and J. Huelsmann.
Ueber ein Modell der Investitionsprogrammplanung unter
Unsicherheit.
In Oper. Res. Verf. 18, VI. Oberwolfach-Tag. Oper. Res. 1973,
141-146, 1974.
Raymond Hemmecke and Rüdiger Schultz.
Decomposition methods for two-stage stochastic integer programs.
In Online optimization of large scale systems, pages 601-622.
Springer, Berlin, 2001.
Raymond Hemmecke and Rüdiger Schultz.
Decomposition of test sets in stochastic integer programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2001.
A.L. Hempenius.
A note on the relationship between the feasible regions of several
types of chance constraints.
Statistica Neerlandica, 27:185-186, 1973.
P. Hénaff.
Hedging exotic derivatives through stochastic optimization.
J. Econom. Dynam. Control, 22(8-9):1453-1466, 1998.
Algorithms and economic dynamics (Geneva, 1996).
Alena Henclova.
Notes on free lunch in the limit and pricing by conjugate duality
theory.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Eligius M. T. Hendrix and Olivier Klepper.
On uniform covering, adaptive random search and raspberries.
J. Global Optim., 18(2):143-163, 2000.
Eligius M. T. Hendrix, P. M. Ortigosa, and I. García.
On success rates for controlled random search.
J. Global Optim., 21(3):239-263, 2001.
International Workshop on Global Optimization, Part 3 (Florence,
1999).
R. Henrion.
On the connectedness of probabilistic constraint sets.
J. Optim. Theory Appl., 112(3):657-663, 2002.
René Henrion.
Characterization of stability for cone increasing constraint
mappings.
Set-Valued Anal., 5(4):323-349, 1997.
René Henrion.
A note on the connectedness of chance constraints.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
René Henrion.
Qualitative stability of convex programs with probabilistic
constraints.
In Optimization (Namur, 1998), pages 164-180. Springer,
Berlin, 2000.
René Henrion.
Structure and stability of probabilistic storage level constraints.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
René Henrion.
Structure and stability of programs with probabilistic storage level
constraints.
In Stochastic optimization techniques (Neubiberg/Munich, 2000),
volume 513 of Lecture Notes in Econom. and Math. Systems, pages 3-20.
Springer, Berlin, 2002.
René Henrion.
Perturbation analysis of chance-constrained programs under variation
of all constraint data.
In Dynamic stochastic optimization (Laxenburg, 2002), volume
532 of Lecture Notes in Econom. and Math. Systems, pages 257-274.
Springer, Berlin, 2004.
René Henrion.
Structural properties of linear probabilistic constraints.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
René Henrion.
Some remarks on value-at-risk optimization.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
René Henrion, Christian Küchler, and Werner Römisch.
Scenario reduction in stochastic programming with respect to
discrepancy distances.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
René Henrion, Pu Li, Andris Möller, Moritz Wendt, and Günter Wozny.
Optimal control of a continuous distillation process under
probabilistic constraints.
In Online optimization of large scale systems, pages 499-517.
Springer, Berlin, 2001.
René Henrion, Li Pu, Andris Möller, Marc C. Steinbach, Moritz Wendt,
and Günter Wozny.
Stochastic optimization for operating chemical processes under
uncertainty.
In Online optimization of large scale systems, pages 457-478.
Springer, Berlin, 2001.
René Henrion and Werner Römisch.
Metric regularity and quantitative stability in stochastic programs
with probabilistic constraints.
Math. Program., 84(1, Ser. A):55-88, 1999.
René Henrion and Werner Römisch.
Stability of solutions to chance constrained stochastic programs.
In Parametric optimization and related topics, V (Tokyo, 1997),
pages 95-114. Lang, Frankfurt am Main, 2000.
René Henrion and Werner Römisch.
Hölder and Lipschitz stability of solution sets in programs
with probabilistic constraints.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
René Henrion and Werner Römisch.
Lipschitz and differentiability properties of quasi-concave and
singular normal distribution functions.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
René Henrion and Cyrille Strugarek.
Convexity of chance constraints with independent random variables.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
René Henrion and Tamas Szantai.
Properties and Calculation of Singular Normal Distributions.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Antonio Heras and Ana G. Aguado.
Stochastic goal programming.
CEJOR Cent. Eur. J. Oper. Res., 7(3):139-158, 1999.
Ulrich Herkenrath and Radu Theodorescu.
On a stochastic approximation procedure applied to the bandit
problem.
Elektron. Informationsverarb. Kybernet., 15(5-6):301-307,
1979.
Onesimo Hernandez-Lerma.
Approximation and adaptive policies in discounted dynamic
programming.
Bol. Soc. Mat. Mex., II. Ser. 30, 25-35, 1985.
Onésimo Hernández-Lerma.
Stochastic optimal control and infinite linear programming.
In Second Symposium on Probability Theory and Stochastic
Processes. First Mexican-Chilean Meeting on Stochastic Analysis (Spanish)
(Guanajuato, 1992), volume 7 of Aportaciones Mat. Notas
Investigación, pages 109-120. Soc. Mat. Mexicana, México City, 1992.
Morgan Herndon, Cary Perttunen, and Bruce Stuckman.
Global optimization of a random walk function.
Informatica, 3(2):198-224, 283, 291, 1992.
Christian Hess.
Convergence of conditional expectations for unbounded random sets,
integrands, and integral functionals.
Math. Oper. Res., 16(3):627-649, 1991.
G.A. Heuer.
On completely mixed strategies in bimatrix games.
J. London math. Soc., II. Ser. 11, 17-20, 1975.
Daniel P. Heyman and Matthew J. Sobel.
Stochastic models in operations research., volume II:
Stochastic optimization. of McGraw-Hill series in quantitative methods
for management.
New York etc.: McGraw-Hill book company., 1984.
Norio Hibiki.
Multi-period stochastic programming models using simulated paths for
strategic asset allocation.
J. Oper. Res. Soc. Japan, 44(2):169-193, 2001.
J.L. Higle and S. Sen.
Epigraphical nesting: a unifying theory for the convergence of
algorithms.
Journal of Optimization Theory and Applications 84:339-360,
1995.
J.L. Higle and S. Sen.
Stochastic decomposition: A statistical method for large scale
stochastic linear programming.
Kluwer Academic Publishers, Dordrecht, 1996.
Julia L. Higle, James C. Bean, and Robert L. Smith.
Deterministic equivalence in stochastic infinite horizon problems.
Math. Oper. Res., 15(3):396-407, 1990.
Julia L. Higle, Wing W. Lowe, and Ronald Odio.
Conditional stochastic decomposition: An algorithmic interface for
optimization and simulation.
Oper. Res. 42, No.2, 311-322, 1994.
Julia L. Higle and Suvrajeet Sen.
Statistical verification of optimality conditions for stochastic
programs with recourse.
Ann. Oper. Res., 30(1-4):215-240, 1991.
Stochastic programming, Part I (Ann Arbor, MI, 1989).
Julia L. Higle and Suvrajeet Sen.
Stochastic decomposition: an algorithm for two-stage linear programs
with recourse.
Math. Oper. Res., 16(3):650-669, 1991.
Julia L. Higle and Suvrajeet Sen.
On the convergence of algorithms with implications for stochastic and
nondifferentiable optimization.
Math. Oper. Res., 17(1):112-131, 1992.
Julia L. Higle and Suvrajeet Sen.
Finite master programs in regularized stochastic decomposition.
Math. Programming, 67(2, Ser. A):143-168, 1994.
Julia L. Higle and Suvrajeet Sen.
Statistical approximations for recourse constrained stochastic
programs.
Ann. Oper. Res., 56:157-175, 1995.
Stochastic programming (Udine, 1992).
Julia L. Higle and Suvrajeet Sen.
Duality and statistical tests of optimality for two stage stochastic
programs.
Math. Programming (Ser. B), 75(2):257-275, 1996.
Approximation and computation in stochastic programming.
Julia L. Higle and Suvrajeet Sen.
Stochastic decomposition, volume 8 of Nonconvex
Optimization and its Applications.
Kluwer Academic Publishers, Dordrecht, 1996.
A statistical method for large scale stochastic linear programming.
Julia L. Higle and Suvrajeet Sen.
Statistical approximations for stochastic linear programming
problems.
Ann. Oper. Res., 85:173-192, 1999.
Stochastic programming. State of the art, 1998 (Vancouver, BC).
Julia L. Higle and Suvrajeet Sen.
Algorithmic implications of duality in stochastic programs.
In System modelling and optimization (Cambridge, 1999), pages
179-188. Kluwer Acad. Publ., Boston, MA, 2000.
Julia L. Higle and Suvrajeet Sen.
Multistage stochastic convex programs: duality and its implications.
Ann. Oper. Res., 142:129-146, 2006.
Julia L. Higle and Lei Zhao.
Adaptive and nonadaptive samples in solving stochastic linear
programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Regina Hildenbrandt.
Kostengünstige Behandlung stochastischer Schwankungen der
Bedarfsgrößen beim Transportproblem durch ein nichtlineares
diskretes Optimierungsproblem.
Optimization, 20(3):335-353, 1989.
Raymond R. Hill and Charles H. Reilly.
Multivariate composite distributions for coefficients in synthetic
optimization problems.
European J. Oper. Res., 121(1):64-77, 2000.
S.D. Hill and M.C. Fu.
Transfer optimization via simultaneous perturbation stochastic
approximation.
In C. Alexopoulos, K. Kang, W.R. Lilegdon, and D. Goldsman, editors,
Proceedings of the Winter Simulation Conference, pages 242-249, 1995.
Stacy D. Hill.
Reduced gradient computation in prediction error identification.
IEEE Trans. Autom. Control AC-30, 776-778, 1985.
Randall S. Hiller and Jonathan Eckstein.
Stochastic dedication: Designing fixed income portfolios using
massively parallel Benders decomposition.
Manage. Sci. 39, No.11, 1422-1438, 1993.
Petri Hilli, Matti Koivu, Teemu Pennanen, and Antero Ranne.
A stochastic programming model for asset liability management of a
finnish pension company.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
A.L. Hillman and P.L. Swan.
Club participation under uncertainty.
Econom. Lett., 4(4):307-312, 1979/80.
K. Hinderer.
Neuere Resultate in der stochastischen dynamischen Optimierung.
Z. Angew. Math. Mech., 55(4):T16-T26, 1975.
K. Hinderer and M. Stieglitz.
A dynamic multi-item two-activity problem.
OR Spektrum 15, No.2, 83-94, 1993.
Karl Hinderer.
Instationaere dynamische Optimierung bei schwachen Voraussetzungen
ueber die Gewinnfunktionen.
Abh. Math. Sem. Univ. Hamburg 36, 208-223, 1971.
Karl Hinderer and Michael Stieglitz.
Increasing and Lipschitz continuous minimizers in one-dimensional
linear-convex systems without constraints: the continuous and the discrete
case.
Math. Methods Oper. Res., 44(2):189-204, 1996.
K.F. Hinderer.
On the structure of solutions of stochastic dynamic programs.
In Proceedings of the seventh conference on probability theory
(Bra sov, 1982), pages 173-182, Utrecht, 1985. VNU Sci. Press.
Ayman Hindy, Chi-fu Huang, and Steven H. Zhu.
Numerical analysis of a free-boundary singular control problem in
financial economics.
J. Econom. Dynam. Control, 21(2-3):297-327, 1997.
Ja. Hion.
Ueber die Ganzzahligkeit der Ecken eines konvexen Polyeders.
Ucenye Zapiski Tartu. gosudarst. Univ. 305, Trudy Mat. Meh. 12,
259-268, 1972.
J. Hirche.
Basishäufigkeit bei linearen Restriktionen.
Math. Operationsforsch. Statist. Ser. Optim., 10(1):27-37,
1979.
J.-B. Hiriart-Urruty.
Conditions nécessaires d'optimalité pour un programme
stochastique avec recours.
SIAM J. Control Optimization, 16(2):317-329, 1978.
J.-B. Hiriart-Urruty.
Extension of Lipschitz integrands and minimization of nonconvex
integral functionals. Applications to the optimal recourse problem in
discrete time.
Probab. Math. Statist., 3(1):19-36 (1983), 1982.
J.B. Hiriart-Urruty.
Algorithms of penalization type and of dual type for the solution of
stochastic optimization problems with stochastic constraints.
In Recent Dev. Stat., Proc. Eur. Meet. Stat. Grenoble 1976,
183-219, 1977.
Jean-Baptiste Hiriart-Urruty.
étude de quelques propriepergeometric integral.
Trudy Sem. Kraev. Zadacam, 10:90-94, 1973.
Jean-Baptiste Hiriart-Urruty.
Etude de quelques proprietes de la fonctionnelle moyenne et de
l'inf- convolution continue en analyse convexe stochastique.
C. r. Acad. Sci., Paris, Ser. A 280, 129-132, 1975.
Jean-Baptiste Hiriart-Urruty.
About properties of the mean value functional and of the continuous
infimal convolution in stochastic convex analysis.
In Optim. Tech., Part 2, Proc. 7th IFIP Conf., Nice 1975, Lect.
Notes Comput. Sci 41, 763-789, 1976.
Jean-Baptiste Hiriart-Urruty.
Algorithmes stochastiques de type pénalisation et de type dual pour
les problèmes avec observations bruitées sur l'objectif et sur les
contraintes.
C.R. Acad. Sci. Paris Sér. A-B, 282(16):Aiii, A907-A910,
1976.
Jean-Baptiste Hiriart-Urruty.
Conditions nécessaires d'optimalité pour un programme
stochastique avec recours.
C.R. Acad. Sci. Paris Ser. A-B, 283(13):Aii, A943-A946, 1976.
Jean-Baptiste Hiriart-Urruty.
Contributions à la programmation mathématique: cas
déterministe et stochastique.
Université de Clermont-Ferrand II, Clermont, 1977.
Thèse présentée à l'Université de Clermont-Ferrand II pour
obtenir le grade de Docteur ès Sciences Mathématiques, Série E, No.
247.
M. Hlynka and J.N. Sheahan.
The secretary problem for a random walk.
Stochastic Process. Appl., 28(2):317-325, 1988.
Y.-C. Ho and D. L. Pepyne.
Simple explanation of the no free lunch theorem of optimization.
Kibernet. Sistem. Anal., (2):164-172, 191, 2002.
Y.C. Ho and T.S. Chang.
Another look at the nonclassical information structure problem.
IEEE Trans. Autom. Control AC-25, 537-540, 1980.
Yu-Chi Ho and Shu Li.
Extensions of infinitesimal perturbation analysis.
IEEE Trans. Autom. Control AC-33, No.5, 427-438, 1988.
B. F. Hobbs and Y. D. Ji.
Stochastic programming-based bounding of expected production costs
for multiarea electric power systems.
Operations Research, 47(6):836-848, 1999.
R. Hochreiter, G. Ch. Pflug, and D. Wozabal.
Multi-stage stochastic electricity portfolio optimization in
liberalized energy markets.
In System modeling and optimization, volume 199 of IFIP
Int. Fed. Inf. Process., pages 219-226. Springer, New York, 2006.
Ronald Hochreiter.
Scenario Optimization for Multi-Stage Stochastic Programming
Problems.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
A. Hoffman, J. Lee, and J. Williams.
New upper bounds for maximum-entropy sampling.
In mODa 6-advances in model-oriented design and analysis
(Puchberg/Schneeberg, 2001), Contrib. Statist., pages 143-153. Physica,
Heidelberg, 2001.
Matthias Hoffmann.
Verallgemeinerung eines stochastischen Quasigradientenverfahrens
und seine Anwendung auf die Optimierung eines Bediensystems mit
unbekannten parametern.
Math. Operationsforsch. Statist. Ser. Optim., 14(1):91-109,
1983.
Balder von Hohenbalken and Gerhard Tintner.
Mathematische Programmierung und ihre Anwendung auf die Wirtschaft.
Z. Nationaloekonomie 34, 1-44, 1974.
John H. Holland.
Genetic algorithms and the optimal allocation of trials.
SIAM J. Computing 2, 88-105, 1973.
Kaj Holmberg.
Efficient decomposition and linearization methods for the stochastic
transportation problem.
Comput. Optim. Appl., 4(4):293-316, 1995.
Kaj Holmberg and Kurt O. Joernsten.
Cross decomposition applied to the stochastic transportation
problem.
Eur. J. Oper. Res. 17, 361-368, 1984.
K. Holmqvist, A. Migdalas, and P.M. Pardalos.
Greedy randomized adaptive search for a location problem with
economies of scale.
In Developments in global optimization (Szeged, 1995),
volume 18 of Nonconvex Optim. Appl., pages 301-313. Kluwer Acad.
Publ., Dordrecht, 1997.
Tito Homem-de-Mello.
On the convergence of simulated annealing for discrete stochastic
optimization.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Tito Homem-de Mello.
Estimation of derivatives of nonsmooth performance measures in
regenerative systems.
Math. Oper. Res., 26(4):741-768, 2001.
Tito Homem-de Mello.
Monte Carlo methods for discrete stochastic optimization.
In Stochastic optimization: algorithms and applications
(Gainesville, FL, 2000), pages 97-119. Kluwer Acad. Publ., Dordrecht, 2001.
Tito Homem-de Mello.
On rates of convergence for stochastic optimization problems under
non-i.i.d. sampling.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
W. Honeit.
Zum mathematischen Begriff des mehrstufigen
Optimalsteuerungsproblems.
Wiss. Z., Tech. Hochsch. Leipz. 6, 167-182, 1982.
Han Hoogeveen and Marjan Van den Akker.
Getting rid of stochasticity: applicable sometimes.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
E. Höpfinger.
Dynamic programming of stochastic activity networks with cycles.
In Optimization techniques (Proc. Ninth IFIP Conf., Warsaw,
1979), Part 2, volume 23 of Lecture Notes in Control and Information
Sci., pages 309-315, Berlin, 1980. Springer.
H.S. Hopkins.
Experimental measurement of a 4-D phase space map of a heavy ion
beam.
Ph.D. thesis, Dept. of Nuclear Engineering, University of California,
Berkeley, December 1997.
A. Hordijk and L.C.M. Kallenberg.
Linear programming and Markov decision processes.
In Oper. Res. Verf. 28, 2nd Symp. Oper. Res., Teil 1, Aachen
1977, 400-406 , 1978.
Arie Hordijk.
On the convergence of the average expected return in dynamic
programming.
J. math. Analysis Appl. 46, 542-544, 1974.
V.Joseph Hotz and Robert A. Miller.
Conditional choice probabilities and the estimation of dynamic
models.
Rev. Econ. Stud. 60, No.3, 497-529, 1993.
J. V. Howard.
Rendezvous search on the interval and the circle.
Oper. Res., 47(4):550-558, 1999.
H.R. Howson and N.G.F. Sancho.
A new algorithm for the solution of multi-state dynamic programming
problems.
Math. Programming, 8:104-116, 1975.
Kjetil Høyland, Michal Kaut, and Stein W. Wallace.
A heuristic for moment-matching scenario generation.
Computational Optimization and Applications, 24(2-3):169-185,
2003.
Wei Shen Hsia.
Bounds for the solution of a stochastic program with recourse.
Bull. Inst. Math. Acad. Sinica, 4(2):307-311, 1976.
Wei Shen Hsia.
On a stochastic program with simple recourse.
J. Math. Anal. Appl., 58(3):705-712, 1977.
Wei Shen Hsia.
Probability density function of a stochastic linear programming
problem.
Naval Res. Logist. Quart., 24(3):417-424, 1977.
Wei Shen Hsia.
On a quadratic stochastic program with simple recourse.
Bull. Inst. Math. Acad. Sinica, 7(1):87-97, 1979.
Jin Yan Hu and Shao Lin Ji.
A backward stochastic differential equation method for a stochastic
optimization problem.
Shandong Daxue Xuebao Ziran Kexue Ban, 36(3):282-286, 2001.
C.C. Huang, I. Vertinsky, and W.T. Ziemba.
Sharp bounds on the value of perfect information.
Operations Res., 25(1):128-139, 1977.
C.C. Huang, D.A. Wehrung, and W.T. Ziemba.
A homogeneous distribution problem with applications to finance.
Management Sci. 23, 297-304, 1976.
C.C. Huang, W.T. Ziemba, and A. Ben-Tal.
Bounds on the expectation of a convex function of a random variable:
with applications to stochastic programming.
Operations Res., 25(2):315-325, 1977.
Haijun Huang, Shouyang Wang, and Michael G. H. Bell.
A bi-level formulation and quasi-Newton algorithm for stochastic
equilibrium network design problem with elastic demand.
J. Syst. Sci. Complex., 14(1):40-53, 2001.
Kai Huang and Shabbir Ahmed.
The value of multi-stage stochastic programming in capacity planning
under uncertainty.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Nan-Jing Huang.
A new class of random completely generalized strongly nonlinear
quasi-complementarity problems for random fuzzy mappings.
Korean J. Comput. Appl. Math., 5(2):315-329, 1998.
Siming Huang.
Average number of iterations of some polynomial
interior-point-algorithms for linear programming.
Sci. China Ser. A, 43(8):829-835, 2000.
G. Hübner.
Sequential similarity transformation for solving finite-stage
sub-Markov decision problems.
In Third Symposium on Operations Research (Univ. Mannheim,
Mannheim, 1978), Section 3, volume 33 of Operations Res. Verfahren,
pages 197-207. Hain, Königstein/Ts., 1979.
Gerhard Hübner.
On the fixed points of the optimal reward operator in stochastic
dynamic programming with discount factor greater than one.
Z. Angew. Math. Mech., 57(8):477-480, 1977.
J. Huelsmann.
Spieltheoretische Ansaetze im stochastischen linearen Optimieren.
In Oper. Res.-Verfahren 10, 236-243, 1970.
J. Huelsmann.
Optimale Entscheidungen im zweistufigen stochastischen Programmieren
als Loesung linearer Programme.
In Operations Res.-Verf. 14, 316-321, 1972.
J. Hülsmann.
Zweistufiges stochastisches Programmieren bei Unsicherheit über
die Verteilung von (A, b, c).
In Vierte Oberwolfach-Tagung über Operations Research (1971),
Teil 2, pages 236-245. Operations Research-Verfahren, XIII, Meisenheim am
Glan, 1972. Hain.
Suwarna Hulsurkar, M.P. Biswal, and S.B. Sinha.
Fuzzy programming approach to multi-objective stochastic linear
programming problems.
Fuzzy Sets and Systems, 88(2):173-181, 1997.
David G. Humphrey and James R. Wilson.
A revised simplex search procedure for stochastic simulation response
surface optimization.
INFORMS J. Comput., 12(4):272-283, 2000.
Yong Liang Huo, San Yang Liu, and Li Yu.
Hausdorff convergence of e-approximate optimal solution
sets in stochastic programming.
Math. Appl. (Wuhan), 19(4):852-856, 2006.
Mohammad Lotfy Hussein and Mohamed Abd El-Hady Kassem.
Interactive stability of multiobjective nonlinear programming
problems with stochastic parameters.
J. Fuzzy Math., 4(3):503-512, 1996.
Thong Nguyen Huu and Hao Tran Van.
A stochastic algorithm for engineering optimization problems.
Optimization Online, http://www.optimization-online.org, 2007.
Chii-Ruey Hwang and Shuenn Jyi Sheu.
Singular perturbed Markov chains and exact behaviors of simulated
annealing processes.
J. Theoret. Probab., 5(2):223-249, 1992.
Masaaki Ida.
Optimality on possibilistic linear programming with normal
possibility distribution coefficient.
Japanese J. Fuzzy Theory Systems, 7(3):349-360 (1996), 1995.
M.G. Ierapetritou and E.N. Pistikopoulos.
Global optimization for stochastic planning, scheduling and design
problems.
In Global optimization in engineering design, volume 9 of
Nonconvex Optim. Appl., pages 231-287. Kluwer Acad. Publ., Dordrecht, 1996.
G. Igelmund and F.J. Radermacher.
Preselective strategies for the optimization of stochastic project
networks under resource constraints.
Networks, 13(1):1-28, 1983.
Koji Iida, Ryusuke Hohzaki, and Takesi Kaiho.
Optimal investigating search maximizing the detection probability.
J. Oper. Res. Soc. Japan, 40(3):294-309, 1997.
Tetsuo Iida.
A non-stationary periodic review production-inventory model with
uncertain production capacity and uncertain demand.
European J. Oper. Res., 140(3):670-683, 2002.
Dan V. Iliescu and Viorel Gh. Voda.
Some notes on Pareto distribution.
Rev. Roumaine Math. Pures Appl., 24(4):597-609, 1979.
E.V. Iljin, A.V. Medvedev, and N.F. Novikov.
On nonparametric algorithms of optimization.
In Modelling and optimization of complex systems (Proc. IFIP-TC
7 Working Conf., Novosibirsk, 1978), volume 18 of Lecture Notes in
Control and Information Sci., pages 198-206, Berlin, 1979. Springer.
B. Illing.
Spieltheoretische Problemstellungen in der stochastischen linearen
Optimierung.
In XV. Int. Wiss. Kolloquium Tech. Hochsch. Ilmenau 1970, Abt.
A, 9-13 , 1970.
B. Illing.
Zur Anwendung spieltheoretischer Methoden in der stochastischen
linearen Optimierung.
Wiss. Z. Tech. Hochsch. Ilmenau 16, No.2/3, 11-16, 1970.
G. Infanger.
Planning under Uncertainty: Solving Large-Scale Stochastic
Linear Programs.
Boyd and Fraser, Danvers, 1994.
G. Infanger and D.P. Morton.
Cut sharing for multistage stochastic linear programs with interstage
dependency.
Mathematical Programming, 75(2):241-256, 1996.
Gerd Infanger.
Monte Carlo (importance) sampling within a Benders decomposition
algorithm for stochastic linear programs.
Ann. Oper. Res., 39(1-4):69-95 (1993), 1992.
R. Infante Macías.
Note on stochastic linear programming: evolution and present status.
I.
Trabajos Estadíst., 23(1-2):9-49, 1971.
M. Inuiguchi.
Stochastic programming problems versus fuzzy mathematical programming
problems.
Japanese J. Fuzzy Theory Systems, 4(1):97-109, 1992.
Masahiro Inuiguchi and Masatoshi Sakawa.
A possibilistic linear program is equivalent to a stochastic linear
program in a special case.
Fuzzy Sets and Systems, 76(3):309-317, 1995.
A.D. Ioffe and D.B. Judin.
Some non-linear problems o f stochastic programming.
U.S.S.R. Comput. Math. Math. Phys. 1 No.1, 207-225 (1972).,
1970.
Hisao Ishibuchi and Hideo Tanaka.
Interval 0-1 programming problem and product-mix analysis.
J. Oper. Res. Soc. Japan 32, No.3, 352-370, 1989.
H. Ishii and T. Matsutomi.
Confidence regional method of stochastic spanning tree problem.
Math. Comput. Modelling, 22(10-12):77-82, 1995.
Stochastic models in engineering, technology and management (Gold
Coast, 1994).
Hiroaki Ishii and Toshio Nishida.
Stochastic linear knapsack problem.
Tech. Rep. Osaka Univ., 32(1630-1651):25-30, 1982.
Hiroaki Ishii and Toshio Nishida.
Stochastic bottleneck spanning tree problem.
Networks 13, 443-449, 1983.
Hiroaki Ishii and Toshio Nishida.
Stochastic linear knapsack problem: probability maximization model.
Math. Japon., 29(2):273-281, 1984.
Hiroaki Ishii and Toshio Nishida.
The stochastic linear continuous type knapsack problem: a generalized
P model.
European J. Oper. Res., 19(1):118-124, 1985.
Hiroaki Ishii, Toshio Nishida, and Yasunori Nanbu.
A generalized chance constraint programming problem.
J. Operations Res. Soc. Japan, 21(1):124-168, 1978.
Hiroaki Ishii and Sh¯ogo Shiode.
Chance constrained bottleneck spanning tree problem.
Ann. Oper. Res., 56:177-187, 1995.
Stochastic programming (Udine, 1992).
Hiroaki Ishii, Sh¯ogo Shiode, and Toshio Nishida.
Chance constrained spanning tree problem.
J. Oper. Res. Soc. Japan, 24(2):147-158, 1981.
Hiroaki Ishii, Sh¯ogo Shiode, Toshio Nishida, and Yoshikazu Namasuya.
Stochastic spanning tree problem.
Discrete Appl. Math., 3(4):263-273, 1981.
Hiroaki Ishii, Syogo Shiode, Toshio Nishida, and Katsurou Iguchi.
An algorithm for a partially chance-constrained E-model.
J. Oper. Res. Soc. Japan, 22(3):233-256, 1979.
Maged George Iskander.
On solving stochastic fuzzy goal programming problem using a
suggested interactive approach.
J. Fuzzy Math., 9(2):437-446, 2001.
Maged George Iskander.
A fuzzy weighted additive approach for stochastic fuzzy goal
programming.
Appl. Math. Comput., 154(2):543-553, 2004.
Maged George Iskander.
Exponential membership function in stochastic fuzzy goal programming.
Appl. Math. Comput., 173(2):782-791, 2006.
Hiroyuki Itami.
Expected objective value of a stochastic linear program and the
degree of uncertainty of parameters.
Management Sci., 21(3):291-301, 1974/75.
M.N. Itbaev.
Duality in problems of inexact and generalized linear programming.
Izv. Akad. Nauk Kazakh. SSR Ser. Fiz.-Mat., 5:35-38, 1985.
Alfredo N. Iusem.
On dual convergence and the rate of primal convergence of Bregman's
convex programming method.
SIAM J. Optim., 1(3):401-423, 1991.
N.A. Ivanchuk.
Analysis of the efficiency of some controlled search procedures.
Problems Inform. Transmission, 19(1):25-33, 1983.
Viktor Ivanovich Ivanenko.
Information problems in control theory.
Prace Nauk. Inst. Cybernet. Tech. Politech. Wroclaw. Ser.
Konfer., 74(31):24-32, 1986.
V.M. Ivanin.
An estimate of the mathematical expectation of the number of elements
of the Pareto set.
Kibernetika (Kiev), 3:145-146, 1975.
S.L. Ivanov.
A method of search for a limit saddle point.
Zh. Vychisl. Mat. i Mat. Fiz., 26(6):939-941, 960, 1986.
P.A. Ivashchenko.
Adaptation in Economics.
"Vishcha Shkola", Kharkov, 1986.
(in Russian).
E.A. Ivushkina.
Mathematical programming with randomness and fuzziness.
In Fuzzy systems: modeling of structure and optimization
(Russian), pages 76-82, Kalinin, 1987. Kalinin. Gos. Univ.
Seiichi Iwamoto.
Linear programming on recursive additive dynamic programming.
J. Operations Res. Soc. Japan 18, 125-151, 1975.
Seiichi Iwamoto.
Conditional decision processes with recursive function.
J. Math. Anal. Appl., 230(1):193-210, 1999.
Seiichi Iwamoto.
Recurrence formulas and decision tree tables in stochastic
optimization.
S¯urikaisekikenky¯usho K¯oky¯uroku, (1132):15-23, 2000.
Mathematical decision theory under uncertainty and ambiguity
(Japanese) (Kyoto, 1999).
Seiichi Iwamoto.
Fuzzy dynamic programming in the stochastic environment.
In Dynamical aspects in fuzzy decision making, volume 73 of
Stud. Fuzziness Soft Comput., pages 27-51. Physica, Heidelberg, 2001.
Seiichi Iwamoto.
Recursive method in stochastic optimization under compound criteria.
In Advances in mathematical economics, Vol. 3, pages 63-82.
Springer, Tokyo, 2001.
Kakuzo Iwamura and Baoding Liu.
A genetic algorithm for chance constrained programming.
J. Inform. Optim. Sci., 17(2):409-422, 1996.
V.S. Izhutkin.
Reduced gradient method for stochastic optimization problem with
nonlinear constraints.
Mosc. Univ. Comput. Math. Cybern. 1989, No.2, 102-104
translation from Vestn. Mosk. Univ., Ser. XV 1989, No.2, 79-81 (1989).,
1989.
Tommi Jaakkola, Michael I. Jordan, and Satinder P. Singh.
On the convergence of stochastic iterative dynamic programming
algorithms.
Neural Comput. 6, No.6, 1185-1201, 1994.
Jihad Jaam.
Ramsey numbers by stochastic algorithms with new heuristics.
In Combinatorics and computer science (Brest, 1995), volume
1120 of Lecture Notes in Comput. Sci., pages 163-181, Berlin, 1996.
Springer.
D. Jacobs, C.-T. Kuo, J.-T. Lim, and S.M. Meerkov.
Improvability of serial production lines: theory and applications.
In Recent advances in control and optimization of manufacturing
systems, volume 214 of Lecture Notes in Control and Inform. Sci.,
pages 121-143. Springer, London, 1996.
J. Jacobs, G. Freeman, J. Grygier, D. Morton, G. Schultz, K. Staschus, and
J. Stedinger.
Socrates: A system for scheduling hydroelectric generation under
uncertainty.
Annals of Operations Research, 59:99-133, 1995.
O.L.R. Jacobs, M.H.A. Davis, M.A.H. Dempster, C.J. Harris, and P.C. Parks,
editors.
Analysis and Optimization of Stochastic Systems.
Academic Press, London, 1980.
Sheldon H. Jacobson and Lee W. Schruben.
Techniques for simulation response optimization.
Oper. Res. Lett., 8(1):1-9, 1989.
R. Jagannathan.
Chance-constrained programming with joint constraints.
Operations Res., 22(2):358-372, 1974.
R. Jagannathan.
Minimax procedure for a class of linear programs under uncertainty.
Operations Res., 25(1):173-177, 1977.
R. Jagannathan.
A minimax procedure for a class of stochastic programs.
In Quantitative planning and control, Essays in Honor of W. W.
Cooper, 23-35 , 1979.
R. Jagannathan.
Use of sample information in stochastic recourse and
chance-constrained programming models.
Management Sci., 31(1):96-108, 1985.
R. Jagannathan.
A stochastic geometric programming problem with multiplicative
recourse.
Oper. Res. Lett., 9(2):99-104, 1990.
R. Jagannathan and M.R. Rao.
A class of nonlinear chance-constrained programming models with
joint constraints.
Operations Res. 21, 360-364, 1973.
Raj Jagannathan.
Linear programming with stochastic processes as parameters as applied
to production planning.
Ann. Oper. Res., 30(1-4):107-114, 1991.
Stochastic programming, Part I (Ann Arbor, MI, 1989).
Patrick Jaillet.
Stochastic routing problems.
In Stochastics in combinatorial optimization, Adv. Sch. CISM,
Udine/Italy 1986, 197-213, 1987.
Patrick Jaillet.
Analysis of probabilistic combinatorial optimization problems in
Euclidean spaces.
Math. Oper. Res., 18(1):51-70, 1993.
Patrick Jaillet and Amedeo R. Odoni.
The probabilistic vehicle routing problem.
In Vehicle routing: Methods and studies, Stud. Manage. Sci.
Syst. 16, 293-318 , 1988.
Anjani Jain and John W. Mamer.
Approximations for the random minimal spanning tree with application
to network provisioning.
Oper. Res. 36, No.4, 575-584, 1988.
Andrzej Jakubowski.
Stochastic model of resource allocation to R & D activities under
cost value uncertainty.
In Optim. Techn., Proc. IFIP Conf., Wuerzburg 1977, Part 2,
Lect. Notes Control Inf. Sci. 7, 411-421, 1978.
Udom Janjarassuk and Jeff Linderoth.
Reformulation and sampling to solve a stochastic network interdiction
problem.
Optimization Online, http://www.optimization-online.org, 2006.
O. Janssens de Bisthoven, P. Schuchewytsch, and Y. Smeers.
Power generation planning with uncertain demand.
In Numerical techniques for stochastic optimization, Springer
Ser. Comput. Math. 10, 465-480, 1988.
C. Jansson and S.M. Rump.
Rigorous solution of linear programming problems with uncertain
data.
Z. Oper. Res. 35, No.2, 87-111, 1991.
Elzbieta Jasinska and Leszek Jerzy Jasinski.
The network models of multi-activity projects as stochastic
programming problems.
Przegl. Stat. 28, 215-223, 1981.
Leszek Jerzy Jasi\'nski.
Game theory method in stochastic programming.
Przeglk ad Statyst., 23(1):107-117, 1976.
Leszek Jerzy Jasi\'nski.
A priori information in stochastic programming.
Przeglk ad Statyst., 26(1-2):35-47 (1980), 1979.
Leszek Jerzy Jasinski.
The linear stochastic distribution problem. The Charnes-Cooper
approach.
Przegl. Stat. 37, No.4, 327-338, 1990.
Leszek Jerzy Jasinski.
Stochastic distribution problem. The Dantzig-Madansky approach.
Przegl. Stat. 38, No.1, 71-83, 1991.
Leszek Jerzy Jasi\'nski and Andrzej Tabeau.
Stochastic programming problems with a known probability of
fulfilling a set of constraints.
Przeglk ad Statyst., 28(1-2):107-116 (1982), 1981.
A.I. Jastremskii.
Ueber das einfachste stochastische Interbranchenmodell der
Optimierung.
Kibernetika, Kiev 1973, Nr. 5, 139-140, 1973.
A.I. Jastremskii.
Duality in two-phase stochastic programming and a stochastic linear
production model.
Kibernetika (Kiev), 2:141-145, 1975.
A.I. Jastremskii.
Wachstumstempi und Effektivitaetsnorm im optimalen Plan eines
dynamischen stochastischen Produktionsmodells.
Kibernetika, Kiev 1976, Nr. 3, 110-114, 1976.
A.I. Jastremskii and È. Vol'kenstain.
Randomness of demand and stochastic models of industrial production.
Issled. Operatsii i ASU, 13:112-116, 136, 1979.
A.V. Jazenin.
Man-machine procedures in decision-making problems with fuzzy goals.
In Mathematical methods of optimization and structuring of
systems (Russian), pages 88-92, 184. Kaliningrad. Gos. Univ., Kaliningrad,
1979.
A.V. Jazenin and G.P. Diskant.
A linear decision-making problem with fuzzy goals.
In Mathematical methods of optimization and structuring of
systems (Russian), pages 77-87, 183. Kaliningrad. Gos. Univ., Kaliningrad,
1979.
T.R. Jefferson and C.H. Scott.
Geometric programming with probabilistic decision variables.
J. Austral. Math. Soc. Ser. B, 22(2):229-236, 1980/81.
Larry Jenkins and Murray Anderson.
A multivariate statistical approach to reducing the number of
variables in data envelopment analysis.
European J. Oper. Res., 147(1):51-61, 2003.
Peter Jennergren.
A note on a Dantzig-Wolfe decomposition-like method for solving a
particular resource-allocation problem under uncertainty.
Math. Operationsforsch. Statistik 4, 127-132, 1973.
Max E. Jerrell and Wendy A. Campione.
Global optimization of econometric functions.
J. Global Optim., 20(3-4):273-295, 2001.
Special issue: Applications to economics.
Elizabeth R. Jessup, Dafeng Yang, and Stavros A. Zenios.
Parallel factorization of structured matrices arising in stochastic
programming.
SIAM J. Optim., 4(4):833-846, 1994.
Jully Jeunet and Nicolas Jonard.
Single-point stochastic search algorithms for the multi-level
lot-sizing problem.
Comput. Oper. Res., 32(4):985-1006, 2005.
Nand K. Jha.
Stochastic mathematical modelling and manufacturing cost estimation
in uncertain industrial environment.
Int. J. Prod. Res. 30, No.12, 2755-2771, 1992.
X.D. Ji and B.D. Familoni.
A diagonal recurrent neural network-based hybrid direct adaptive
SPSA control system.
IEEE Transactions on Automatic Control, 44:1469-1473, 1999.
Xiaoyu Ji.
Models and algorithm for stochastic shortest path problem.
Appl. Math. Comput., 170(1):503-514, 2005.
H. Jiang and L. Qi.
Globally and superlinearly convergent trust-region algorithm for
convex sc\sp 1-minimization problems and its application to
stochastic programs.
J. Optim. Theory Appl., 90(3):649-669, 1996.
Henrik Joensson, Kurt Joernsten, and Edward A. Silver.
Application of the scenario aggregation approach to a two-stage,
stochastic, common component, inventory problem with a budget constraint.
Eur. J. Oper. Res. 68, No.2, 196-211, 1993.
Tor A. Johansen, René van de Molengraft, and Henk Nijmeijer, editors.
Switched, piecewise and polytopic linear systems.
Taylor & Francis Ltd., Abingdon, 2002.
Internat. J. Control 75 (2002), no. 16-17.
Andrew E.W. Jones and G.W. Forbes.
An adaptive simulated annealing algorithm for global optimization
over continuous variables.
J. Global Optim., 6(1):1-37, 1995.
Stephen K. Jones, Ralph K. Cavin III, and William M. Reed.
Analysis of error-gradient adaptive linear estimators for a class of
stationary dependent processes.
IEEE Trans. Inf. Theory IT-28, 318-329, 1982.
T. W. Jonsbråten.
Oil field optimization under price uncertainty.
Journal of the Operational Research Society, 49(8):811-818,
1998.
Tore W. Jonsbråten, Roger J.-B. Wets, and David L. Woodruff.
*a class of stochastic programs with decision dependent random
elements.
Ann. Oper. Res., 82:83-106, 1998.
Modelling: in memory of Åsa Hallefjord.
J.S. Jordan.
Information and shadow prices for the constrained concave team
problem.
J. math. Economics 2, 371-393, 1975.
Tibor Jordan and Alessandro Panconesi, editors.
Probabilistic methods in combinatorial optimization.
John Wiley & Sons Inc., New York, 2002.
Random Structures Algorithms 20 (2002), no. 3.
S. Jorjani, C.H. Scott, and D.L. Woodruff.
Selection of an optimal subset of sizes.
International Journal of Production Research, 37:3697-3710,
1999.
Rajani R. Joshi.
A new heuristic algorithm for probabilistic optimization.
Comput. Oper. Res., 24(7):687-697, 1997.
R.R. Joshi and D.K. Satyanarayana.
A nested layered network model for parallel solutions of discrete
SPPs.
Comput. Math. Appl., 33(5):111-123, 1997.
Sumit Joshi.
Recursive utility, martingales, and the asymptotic behaviour of
optimal processes.
J. Econom. Dynam. Control, 21(2-3):505-523, 1997.
Stanislaw F. Jozwiak.
Minimum weight design of structures with random parameters.
Comput. Struct. 23, 481-485, 1986.
È. Jubi.
Finding the optimal randomized solution in M- and P-models of
stochastic programming.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat., 26(1):64-71,
1977.
È. Jubi.
A statistical study and method for the solution of stochastic
programming problems.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat.,
26(4):369-375, 1977.
James V. Jucker and Robert C. Carlson.
The simple plant-location problem under uncertainty.
Operations Res. 2 1045-1055 (1977)., 1976.
Joaquim J. Júdice, Hanif D. Sherali, and Isabel M. Ribeiro.
The eigenvalue complementarity problem.
Comput. Optim. Appl., 37(2):139-156, 2007.
A.D. Judin.
Dualitaet in der mehretappenweisen stochastischen Programmierung.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1973, Nr. 6, 24-31, 1973.
A.D. Judin.
Koordinatenweiser Abstieg in Problemen der unendlichdimensionalen
konvexen Programmierung.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1974, Nr. 1, 34-42, 1974.
D.B. Judin.
Methods of constructing decision rules for multistage problems of
stochastic programming.
Sov. Math., Dokl. 14, 824-828 translation from Dokl. Akad. Nauk
SSSR 210, 779-782 (1973)., 1973.
D.B. Judin.
Multistage problems of stochastic programming.
Sov. Math., Dokl. 14, 787-790 translation from Dokl. Akad. Nauk
SSSR 210, 545-548 (1973)., 1973.
D.B. Judin.
Mathematische Methoden der Steuerung bei unvollstaendiger
Information. Aufgaben und Methoden der stochastischen Programmierung.
(Matematiceskie metody upravlenija v uslovijah nepolnoi informacii. Zadaci i
metody stohasticeskogo programmirovanija.).
Moskau: 'Sovetskoe Radio', 1974.
D.B. Judin and T.D. Beresnewa.
Statische und dynamische Modelle der stochastischen Optimierung.
In Math.-oekon. Meth. Modelle, 105-152, 1977.
D.B. Judin and È. V. Coi.
A priori decision rules in multistage stochastic programming
problems.
Èkonom. i Mat. Metody, 9:947-961, 1973.
D.B. Judin and É. V. Coi.
A qualitative study of certain classes of stochastic problems.
Kibernetika (Kiev), 2:73-77, 1975.
D.B. Judin and E.V. Coi.
Ganzzahlige stochastische Programmierung.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1974, Nr. 1, 3-11, 1974.
D.B. Judin and E.V. Coi.
Qualitative Untersuchung einiger Klassen stochastischer Probleme.
Kibernetika, Kiev 1975, Nr. 2, 73-77, 1975.
Ghassan Kabbara.
New utilization of fuzzy optimization method.
In Fuzzy information and decision processes, 239-246, 1982.
D.G. Kabe.
On some coverage probability maximization problems.
Commun. Stat., Simulation Comput. B9, 73-79, 1980.
Janusz Kacprzyk and Piotr Staniewski.
A new approach to the control of stochastic systems in a fuzzy
environment.
Arch. Automat. Telemech., 25(4):433-444 (1981), 1980.
D.P. Kakabadze, G.M. Kubintsev, and V.A. Taran.
Control of the random step distribution function in a discrete
adaptive system.
Automat. Remote Control, 41(2):217-224, 1980.
V.A. Kaladze and Ya.S. Rubinshtejn.
Convergence of the random search method in the neighborhood of the
extremum.
Avtomat. vycislit. Tehn., Riga 1972, No.3, 67, 1972.
V.A. Kaladze and Ya.S. Rubinshtejn.
Polar modification of random search.
Avtomat. vycislit. Tehn., Riga 1972, No.2, 58, 1972.
Osmo Kaleva.
A note on fixed points for fuzzy mappings.
Fuzzy Sets and Systems, 15(1):99-100, 1985.
È. V. Kalinina, Ja. I. Hurgin, and A.G. Sapiro.
Stable distributions in problems of stochastic programming.
In Mathematical methods of solution of economic problems, No. 8
(Russian), pages 134-138, Moscow, 1979. "Nauka".
P. Kall.
Some remarks on the distribution problem of stochastic linear
programming.
In Operations Res.-Verf. 16, 189-196, 1973.
P. Kall.
Stochastische Optimierung-einige neuere Ergebnisse.
In Fortschritte in der mathematischen Optimierung (Tagung,
Humboldt Univ., Berlin, 1978), volume 15 of Seminarberichte, pages
52-64. Humboldt Univ. Berlin, 1978.
P. Kall.
Solving complete fixed recourse problems by successive
discretization.
In Recent results in stochastic programming, Proc., Oberwolfach
1979, Lect. Notes Econ. Math. Syst. 179, 135-138, 1980.
P. Kall.
Stochastic programming.
European J. Oper. Res., 10(2):125-130, 1982.
P. Kall.
Stochastic programs with recourse: an upper bound and the related
moment problem.
Z. Oper. Res. Ser. A-B, 31(3):A119-A141, 1987.
P. Kall.
Stochastic programming with recourse: upper bounds and moment
problems-a review.
In Advances in mathematical optimization, volume 45 of
Math. Res., pages 86-103, Berlin, 1988. Akademie-Verlag.
P. Kall.
An upper bound for SLP using first and total second moments.
Ann. Oper. Res., 30(1-4):267-276, 1991.
Stochastic programming, Part I (Ann Arbor, MI, 1989).
P. Kall.
A survey of approximation methods in stochastic programming.
In Optimization: models, methods, solutions (Russian) (Irkutsk,
1989), pages 125-133. "Nauka" Sibirsk. Otdel., Novosibirsk, 1992.
P. Kall.
Solution methods in stochastic programming.
In System modelling and optimization (Compiègne, 1993),
volume 197 of Lecture Notes in Control and Inform. Sci., pages 3-22.
Springer, London, 1994.
P. Kall.
Bounds for and approximations to stochastic linear programs with
recourse-tutorial.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 1-21. Springer, Berlin, 1998.
P. Kall and J. Mayer.
A model management system for stochastic linear programming.
In System modelling and optimization (Zurich, 1991), volume 180
of Lecture Notes in Control and Inform. Sci., pages 580-587. Springer,
Berlin, 1992.
P. Kall and J. Mayer.
SLP-IOR: A model management system for stochastic linear
programming - system design.
In A.J.M. Beulens and H.-J. Sebastian, editors,
Optimization-Based Computer-Aided Modelling and Design, pages 139-157.
Springer-Verlag, Berlin etc., 1992.
P. Kall and J. Mayer.
SLP-IOR: on the design of a workbench for testing SLP
codes.
Investigación Oper., 14(2-3):148-161, 1993.
Workshop on Stochastic Optimization: the State of the Art (Havana,
1992).
P. Kall and J. Mayer.
Computer support for modeling in stochastic linear programming.
In Stochastic programming (Neubiberg/München, 1993), volume
423 of Lecture Notes in Econom. and Math. Systems, pages 54-70.
Springer, Berlin, 1995.
P. Kall and J. Mayer.
On testing SLP codes with SLP-IOR.
Manuscript, Inst. Oper. Res., University of Zurich, 1996.
Accepted for Proceedings Mátraháza 1996.
P. Kall and J. Mayer.
On solving stochastic linear programming problems.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 329-344. Springer, Berlin, 1998.
P. Kall and J. Mayer.
On the role of bounds in stochastic linear programming.
Optimization, 47(3-4):287-301, 2000.
Numerical methods for stochastic optimization and real-time control
of robots (Neubiberg/Munich, 1998).
P. Kall and W. Oettli.
Measurability theorems for stochastic extremals.
SIAM J. Control, 13(5):994-998, 1975.
P. Kall and W. Oettli.
Measurability theorems for stochastic extremals (extended abstract).
In Siebente Oberwolfach-Tagung über Operations Research
(1974), Operations Research Verfahren, Band XXI, pages 139-140, Meisenheim
am Glan, 1975. Hain.
P. Kall and Werner Oettli.
Measurability theorems for stochastic extremals.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 93-95, London, 1980. Academic Press.
P. Kall, A. Ruszczy\'nski, and K. Frauendorfer.
Approximation techniques in stochastic programming.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 33-64. Springer, Berlin, 1988.
P. Kall and D. Stoyan.
Solving stochastic programming problems with recourse including error
bounds.
Math. Operationsforsch. Statist. Ser. Optim., 13(3):431-447,
1982.
P. Kall and S.W. Wallace.
Stochastic Programming.
Wiley, Chichester etc., 1994.
Peter Kall.
Approximations to stochastic programs with complete fixed recourse.
Numer. Math., 22:333-339, 1974.
Peter Kall.
Stochastic linear programming.
Springer-Verlag, Berlin, 1976.
Ökonometrie und Unternehmensforschung, No. XXI.
Peter Kall.
Computational methods for solving two-stage stochastic linear
programming problems.
Z. Angew. Math. Phys., 30(2):261-271, 1979.
Peter Kall.
Towards computing the expected value of perfect information.
In Mathematische Systeme in der Oekonomie, R. Henn z. 60. Geb.,
277-287 , 1983.
Peter Kall.
On approximations and stability in stochastic programming.
In Parametric optimization and related topics (Plaue, 1985),
volume 35 of Math. Res., pages 387-407, Berlin, 1987. Akademie-Verlag.
Peter Kall and János Mayer.
SLP-IOR: an interactive model management system for
stochastic linear programs.
Math. Programming, 75(2, Ser. B):221-240, 1996.
Approximation and computation in stochastic programming.
Peter Kall and János Mayer.
Modeling support for multistage recourse problems.
In Dynamic stochastic optimization (Laxenburg, 2002), volume
532 of Lecture Notes in Econom. and Math. Systems, pages 21-41.
Springer, Berlin, 2004.
Peter Kall and János Mayer.
Building and solving stochastic linear programming models with
SLP-IOR.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 79-93. SIAM, Philadelphia, PA, 2005.
Peter Kall and Janos Mayer.
Stochastic Linear Programming: Models, Theory, and Computation,
volume 80 of International Series in Operations Research & Management
Science.
Springer, 2005.
Peter Kall and János Mayer.
Some insights into the solution algorithms for SLP problems.
Ann. Oper. Res., 142:147-164, 2006.
Peter Kall and András Prékopa, editors.
Recent results in stochastic programming, volume 179 of
Lecture Notes in Economics and Mathematical Systems, Berlin, 1980.
Springer-Verlag.
J.G. Kallberg, R.W. White, and W.T. Ziemba.
Short term financial planning under uncertainty.
Manage. Sci. 28, 670-682, 1982.
J.G. Kallberg and W.T. Ziemba.
An extended Frank-Wolfe algorithm with application to portfolio
selection problems.
In Recent results in stochastic programming, Proc., Oberwolfach
1979, Lect. Notes Econ. Math. Syst. 179, 139-162, 1980.
J.G. Kallberg and W.T. Ziemba.
Generalized concave functions in stochastic programming and
portfolio theory.
In Generalized concavity in optimization and economics, Proc.
NATO Adv. Study Inst., Vancouver/Can. 1980, 719-767, 1981.
L. Kallenberg.
Special solution methods for replacement problems.
In Operations Research Proceedings, 2000 (Dresden), pages
87-90, Berlin, 2001. Springer.
L.C.M. Kallenberg.
Z. Oper. Res., (1).
L.C.M. Kallenberg.
Linear programming to compute a bias-optimal policy.
In Operations research proceedings 1981 (Göttingen), pages
433-440. Springer, Berlin, 1982.
L.C.M. Kallenberg.
Survey of linear programming for standard and nonstandard Markovian
control problems. II. Applications.
Z. Oper. Res., 40(2):127-143, 1994.
Markku Kallio and William T. Ziemba.
Arbitrage pricing simplified.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Sofia Kalpazidou.
Optimal transforms by two predictable processes.
Bull. Math. Soc. Sci. Math. Repub. Soc. Roum., Nouv. Ser.
30(78), 313-319, 1986.
Basil A. Kalymon.
An optimization algorithm for a linear model of a simulation
system.
Management Sci., Theory 21, 516-530, 1975.
M.M. Kamilov and Sh. Kh. Fazylov.
Optimization in pattern recognition problems.
In Random search and pattern recognition (Russian), pages
11-17, 118. "Fan", Tashkent, 1985.
V.A. Kaminskas and V.A. Pukas.
On a numerical method for solving a two-stage nonlinear stochastic
problem.
In Issled. Algoritm. Ocen. Paramet. din. Sist. Process., Mater.
Semin. Inst. Fiz. Mat. AN Litov. SSR, Stat. Probl. Upr. 16, 120-127, 1976.
V.N. Kaminskij.
Optimization of closed stochastic networks with exponential
servicing.
Eng. Cybern. 18, No.6, 57-64 translation from Izv. Akad. Nauk
SSSR, Tekh. Kibern. 1980, No.6, 68-76 (1980)., 1980.
T. Kämpke.
Tight bounds for capacities.
Comput. Math. Appl., 27(8):67-86, 1994.
Yu. S. Kan.
Optimization of control by the quantile criterion.
Avtomat. i Telemekh., (5):77-88, 2001.
Yu. S. Kan and A. I. Kibzun.
Sensitivity analysis of worst-case distribution for probability
optimization problems.
In Probabilistic constrained optimization, volume 49 of
Nonconvex Optim. Appl., pages 132-147. Kluwer Acad. Publ., Dordrecht, 2000.
Yu. S. Kan and A.I. Kibzun.
Convexity properties of probability and quantile functions in
optimization problems.
Avtomat. i Telemekh., 3:82-102, 1996.
Yu. S. Kan and A. A. Mistryukov.
On the equivalence in stochastic programming with probability and
quantile objectives.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 145-153. Springer, Berlin, 1998.
Yu. S. Kan and A. V. Rusyaev.
The quantile minimization problem with a bilinear loss function.
Avtomat. i Telemekh., 7:67-75, 1998.
Yu. S. Kan and N. V. Tuzov.
Quantile minimization of the normal distribution of a bilinear loss
function.
Yuri Kan.
Application of the quantile optimization to bond portfolio selection.
In Stochastic optimization techniques (Neubiberg/Munich, 2000),
volume 513 of Lecture Notes in Econom. and Math. Systems, pages
285-308. Springer, Berlin, 2002.
O. N. Kaneva.
A two-stage nonlinear stochastic programming problem with a
deterministic compensation matrix.
In Mathematical structures and modeling. No. 14 (Russian),
pages 25-33. Omsk. Gos. Univ., Omsk, 2004.
I. Ju. Kaniovskaja.
Convergence of random processes generated by a procedure of the
stochastic approximation type.
In Methods of solution of problems of nonsmooth optimization and
stochastic programming (Russian), pages 18-25, 35. Akad. Nauk Ukrain. SSR
Inst. Kibernet., Kiev, 1979.
I. Yu. Kaniovskaya.
Asymptotic properties of Fabian's algorithm with nonsmooth
regression functions.
Issled. Operatsii i ASU, 23:29-35, 139, 1984.
Yu.M. Kaniovski, P.S. Knopov, and Z.V. Nekrylova.
Limit Theorems for Stochastic Programming Processes.
Naukova dumka, Kiev, 1980.
(in Russian).
Yuri M. Kaniovski, Alan J. King, and Roger J.-B. Wets.
Probabilistic bounds (via large deviations) for the solutions of
stochastic programming problems.
Ann. Oper. Res., 56:189-208, 1995.
Stochastic programming (Udine, 1992).
Ju. M. Kaniovskii.
The asymptotic normality of a stochastic minimization method.
In Methods of operations research and of reliability theory in
systems analysis (Russian), pages 63-69, Kiev, 1977. Akad. Nauk. Ukrain.
SSR Inst. Kibernet.
Ju. M. Kaniovskii.
A way of controlling the calculation accuracy in the statistical
gradient method.
Kibernetika (Kiev), 5:87-89, 1978.
Ju. M. Kaniovskii.
Asymptotic properties of a stochastic method of minimization.
In Operations research (models, systems, solutions), No. 7
(Russian), pages 24-36, 1, Moscow, 1979. Akad. Nauk SSSR Vychisl. Tsentr.
Ju. M. Kaniovskii, P.S. Knopov, and Z.V. Nekrylova.
The stochastic programming method in Hilbert space.
Kibernetika (Kiev), 6:71-79, 1978.
Ju. M. Kaniovskii, P.S. Knopov, and Z.V. Nekrylova.
Predel' nye teoremy dlya protsessov stokhasticheskogo
programmirovaniya.
"Naukova Dumka", Kiev, 1980.
Yu. M. Kaniovskii.
Estimating the error in direct methods of stochastic programming.
Cybernetics, 16(5):768-775 (1981), 1980.
Yu. M. Kaniovskii.
Estimation of errors in construction of confidence intervals in some
stochastic programming methods.
Cybernetics, 17(1):121-125, 1981.
Yu. M. Kaniovskii.
A limit theorem for processes of stochastic optimization and
estimation with constant step.
Dokl. Akad. Nauk SSSR, 261(1):18-20, 1981.
Yu. M. Kaniovskii.
Asymptotic properties of a stochastic analogue of the gradient method
of optimization in a Hilbert space.
Issled. Operatsii i ASU, 20:55-69, 1982.
Yu. M. Kaniovskii.
Comparison of the rates of convergence of two-step and one-step
methods of stochastic programming.
Dokl. Akad. Nauk Ukrain. SSR Ser. A, 7:70-73, 1982.
Yu. M. Kaniovskii.
A stochastic analogue of the conjugate gradient method with a fixed
step.
Issled. Operatsii i ASU, 21:3-8, 1983.
Yu. M. Kaniovskii.
Fabian's algorithm in the theory of self-adjusting systems.
Avtomat. i Telemekh., 11:66-69, 1984.
Yu. M. Kaniovskii.
Stochastic optimization algorithms with slowly decreasing step
multiplier.
Kibernetika (Kiev), 4:iii, 102-106, 135, 1984.
Yu. M. Kaniovskii and P.S. Knopov.
Asymptotic properties of some stochastic approximation methods.
Cybernetics, 15(5):727-734 (1980), 1979.
Yu. M. Kaniovskii and G.G. Murauskas.
Asymptotic properties of the stochastic method of Lagrange
multipliers.
In Theory of optimal decisions, pages 78-84, 114, Kiev, 1982.
Akad. Nauk Ukrain. SSR Inst. Kibernet.
Yu.M. Kaniovskij.
Limit theorems for random processes, and stochastic Markov recurrent
procedures.
Cybernetics 15, 917-927 translation from Kibernetika 1979, No.6,
127-130 (1979)., 1980.
Yu.M. Kaniovskij.
On a certain method of random search.
Zh. Vychisl. Mat. Mat. Fiz. 21, 499-503, 1981.
Yu.M. Kaniovskij.
Comparison of the convergence rates of the two-step and one-step
methods of stochastic programming.
Dokl. Akad. Nauk Ukr. SSR, Ser. A 1982, No.7, 70-73, 1982.
Yu.M. Kaniovskij.
Stochastical analogue of the method of conjugated gradients with
constant increment.
Issled. Oper. ASU 21, 3-8, 1983.
Yu.M. Kaniovskij.
The limit theorem for optimization and estimation algorithms with a
variable step.
Dokl. Akad. Nauk Ukr. SSR, Ser. A 1985, No.1, 59-61, 1985.
Yu.M. Kaniovskij, P.S. Knopov, and Z.V. Nekrylova.
Stochastic programming in Hilbert space.
Cybernetics 14, 878-888 translation from Kibernetika, No.6,
71-79 (1978)., 1979.
Yu.M. Kaniovskij, P.S. Knopov, and Z.V. Nekrylova.
Limit theorems for processes of stochastic programming.
(Predel'nye teoremy dlya protsessov stokhasticheskogo programmirovaniya).
Akademiya Nauk Ukrainskoj SSR, Ordena Lenina Institut Kibernetiki.
Kiev: "Naukova Dumka"., 1980.
V. Kanková.
Estimates in stochastic programming-chance constrained case.
Problems Control Inform. Theory/Problemy Upravlen. Teor.
Inform., 18(4):251-260, 1989.
V. Kanková.
A note on multistage stochastic programming with individual
probability constraints.
In Operations Research Proceedings, 2000 (Dresden), pages
91-96, Berlin, 2001. Springer.
V. Kanková.
A remark on the analysis of multistage stochastic programs: Markov
dependence.
ZAMM Z. Angew. Math. Mech., 82(11-12):781-793, 2002.
4th GAMM-Workshop "Stochastic Models and Control Theory"
(Lutherstadt Wittenberg, 2001).
Vlasta Kanková.
An approximative solution of a stochastic optimization problem.
In Transactions of the Eighth Prague Conference on Information
Theory, Statistical Decision Functions, Random Processes (Prague, 1978), Vol.
A, pages 349-353. Reidel, Dordrecht, 1978.
Vlasta Kanková.
Differentiability of the optimal function in a two-stage stochastic
nonlinear programming problem.
Ekonom.-Mat. Obzor, 14(3):322-330, 1978.
Vlasta Kanková.
Optimum solution of a stochastic optimization problem with unknown
parameters.
In Transactions of the Seventh Prague Conference on Information
Theory, Statistical Decision Functions and the Eighth European Meeting of
Statisticians (Tech. Univ. Prague, Prague, 1974), Vol. B, pages 239-244,
Prague, 1978. Academia.
Vlasta Kanková.
Stability in the stochastic programming.
Kybernetika (Prague), 14(5):339-349, 1978.
Vlasta Kanková.
Approximate solution of problems of two-stage stochastic nonlinear
programming.
Ekonom.-Mat. Obzor, 16(1):64-76, 1980.
Vlasta Kanková.
Optimization problem with parameter and its application to the
problems of two-stage stochastic nonlinear programming.
Kybernetika (Prague), 16(5):411-425, 1980.
Vlasta Kanková.
Sequences of stochastic programming problems with incomplete
information.
In Transactions of the ninth Prague conference on information
theory, statistical decision functions, random processes, Vol. A (Prague,
1982), pages 327-332. Reidel, Dordrecht, 1983.
Vlasta Kanková.
Uncertainty in stochastic programming.
In Stochastic optimization (Kiev, 1984), volume 81 of
Lecture Notes in Control and Inform. Sci., pages 393-401. Springer, Berlin,
1986.
Vlasta Kanková.
Empirical estimates in stochastic programming.
In Transactions of the Tenth Prague Conference on Information
Theory, Statistical Decision Functions, Random Processes, Vol. B (Prague,
1986), pages 21-28. Reidel, Dordrecht, 1988.
Vlasta Kanková.
A note on the differentiability in two-stage stochastic nonlinear
programming problems.
Kybernetika (Prague), 24(3):207-215, 1988.
Vlasta Kanková.
Necessary and sufficient optimality conditions for two-stage
stochastic programming problems.
Kybernetika (Prague), 25(5):375-385, 1989.
Vlasta Kanková.
A note on the minimax approach to the stochastic programming
problems.
Ekonom.-Mat. Obzor, 26(1):64-70, 1990.
Vlasta Kanková.
On the convergence rate of empirical estimates in chance constrained
stochastic programming.
Kybernetika (Prague), 26(6):449-461, 1990.
Vlasta Kankova.
A note on the stability in stochastic programming problems.
In Information theory, statistical decision functions, random
processes, Trans. 11th Prague Conf., Prague/Czech. 1990, Vol. B, 51-59,
1992.
Vlasta Kanková.
Stability in stochastic programming-the case of unknown location
parameter.
Kybernetika (Prague), 29(1):80-101, 1993.
Vlasta Kanková.
A note on estimates in stochastic programming.
J. Comput. Appl. Math., 56(1-2):97-112, 1994.
Stochastic programming: stability, numerical methods and applications
(Gosen, 1992).
Vlasta Kanková.
A note on the relationship betweeen distribution function estimation
and estimations in stochastic programming.
In Trans. of the Twelfth Prague Conference, pages 122-125,
1994.
Vlasta Kanková.
On stability in two-stage stochastic nonlinear programming.
In Asymptotic statistics (Prague, 1993), Contrib. Statist.,
pages 329-340. Physica, Heidelberg, 1994.
Vlasta Kanková.
On the stability in stochastic programming-generalized simple
recourse problems.
Informatica, 5(1-2):55-78, 1994.
Vlasta Kanková.
A note on contamination in stochastic programming - special cases,
1996.
Kvantitatívne metódy v ekonomike (viackriteriálna
optimizácia VIII). Slov. spol. pre operacný výzkum
Bratislava.
Vlasta Kanková.
A note on interval estimates in stochastic optimization.
Bulletin of the Czech Econometric Society, 5:63-79, 1996.
Vlasta Kanková.
A note on objective functions in multistage stochastic nonlinear
programming problems.
In System modelling and optimization (Prague, 1995), pages
582-589. Chapman & Hall, London, 1996.
Vlasta Kanková.
Convexity, Lipschitz property and differentiability in two-stage
stochastic nonlinear programming problems.
In Proceedings of the 3rd International Conference on
Approximation and Optimization in the Caribbean (Puebla, 1995), page 17 pp.
(electronic). Benemérita Univ. Autón. Puebla, Puebla, 1997.
Vlasta Kanková.
On an e-solution of minimax problem in stochastic
programming.
In J. Stepán V. Benes, editor, Proceedings of
the 3rd International Conference on Distributions with Given Marginals and
Moment Problems, pages 211-216, Dordrecht-Boston-London, 1997. Kluwer
Academic. Publisher.
Vlasta Kanková.
On estimates in time dependent stochastic optimization.
Zeitschrift für Angewandte Mathematik und Mechanik,
77:587-588, 1997.
Vlasta Kanková.
On the stability in stochastic programming: the case of individual
probability constraints.
Kybernetika (Prague), 33(5):525-546, 1997.
Vlasta Kanková.
A note on empirical estimates and probability multifunctions in
stochastic programming.
In M. Husková, P. Lachout, and J.A. Vísek, editors,
Proceedings of the Conference Prague Stochastics '98, pages 279-284,
Prague, 1998. Union of Czech Mathematicians and Physicists.
Vlasta Kanková.
A note on multifunctions in stochastic programming.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 154-168. Springer, Berlin, 1998.
Vlasta Kanková.
Remarks on contamination in stochastic programming.
Central European Journal for Operations Research and Economics,
6(3-4):215-224, 1998.
Vlasta Kanková.
A note on analysis of economic activities with random elements.
In M. Plevný and V. Friedlich, editors, Proceedings of the
Mathematical Methods in Economics 1998, pages 53-58, Cheb (Czech Republic),
1999. Vydavatelství Západoceské university.
Vlasta Kanková.
A note on multistage stochastic programming.
In Proceedings of the 11th Joint Czech-German- Slovak Conference
"Mathematical Methods in Economy and Industry", pages 45-52, Liberec
(Czech Republic), 1999. Technická univerzita Liberec,
Vysokoskolský podnik.
Vlasta Kanková.
Multistage stochastic programming; stability, approximation and
markov dependence.
Operations Research Proceedings 1999, pages 136-141, 2000.
Vlasta Kanková.
Multistage stochastic programming; stability, approximation and
Markov dependence.
In Operations Research Proceedings 1999 (Magdeburg), pages
136-141, Berlin, 2000. Springer.
Vlasta Kanková.
A remark on multiobjective stochastic optimization problems:
stability and empirical estimates.
In Operations Research Proceedings 2003, pages 379-386,
Berlin, 2004. Springer.
Vlasta Kanková and Petr Lachout.
Convergence rate of empirical estimates in stochastic programming.
Informatica, 3(4):497-523, 595, 603, 1992.
Vlasta Kanková and Karel Sladky.
Risk-sensitive optimality criteria in multistage stochastic
optimization.
In D. Bauerová, J. Hanclová, L. Hrbác,
J. Mockor, and J. Ramík, editors, Proc. of the Mathematical
Methods in Economics. Matematické metody v ekonomii, Ostrava, pages
95-101. VSB, 1997.
Vlasta Kanková and Martin Smíd.
On approximation in multistage stochastic programs: Markov
dependence.
Kybernetika (Prague), 40(5):625-638, 2004.
R. Kannan.
Stochastic approximation and nonlinear operator equations.
In Approximate solution of random equations, pages 87-105, New
York, 1979. North-Holland.
Ravi Kannan, John Mount, and Sridhar Tayur.
A randomized algorithm to optimize over certain convex sets.
Math. Oper. Res., 20(3):529-549, 1995.
A. Kanudia and R. Loulou.
Robust responses to climate change via stochastic MARKAL: The
case of Quebec.
European Journal of Operational Research, 106(1):15-30, 1998.
A. Kanudia and R. Loulou.
Advanced bottom-up modelling for national and regional energy
planning in response to climate change.
International Journal of Environment and Pollution,
12(2-3):191-216, 1999.
A. Kanudia and P. R. Shukla.
Modelling of uncertainties and price elastic demands in
energy-environment planning for India.
Omega-International Journal of Management Science,
26(3):409-423, 1998.
Chiang Kao and Shih-Pin Chen.
A stochastic quasi-Newton method for simulation response
optimization.
European J. Oper. Res., 173(1):30-46, 2006.
Edward P.C. Kao.
A preference order dynamic program for stochastic assembly line
balancing.
Management Sci. 22, 1097-1104, 1976.
Edward P.C. Kao and Maurice Queyranne.
Aggregation in a two-stage stochastic program for manpower planning
in the service sector.
In Delivery of urban services, TIMS Stud. Manage. Sci. 22,
205-225, 1986.
E.P.C. Kao and M. Queyranne.
Budgeting cost of nursing in a hospital.
Management Science, 31(5):608-621, 1985.
Robert S. Kaplan and John V. Soden.
On the objective function for the sequential P-model of
chance-constrained programming.
Operations Res. 19, 105-114, 1971.
A.I. Kaplinskii and A.S. Krasnenker.
The multitest approach to the formation of multilevel algorithms for
stochastic optimization.
Avtomat. i Vycisl. Tehn. (Riga), 4:14-21, 1975.
A.I. Kaplinskii, A.S. Krasnenker, and A.V. Nazin.
Training by the decrease principle in a vector optimization
problem.
Avtom. Vychisl. Tekh. 1978, No.4, 43-47, 1978.
A.I. Kaplinskii, A.S. Krasnenker, and Ya. Z. Tsypkin.
Randomization and smoothing in adaptation problems and algorithms.
Automat. Remote Control, 35(6, part 1):913-921, 1974.
A.I. Kaplinskii, A.S. Poznjak, and A.I. Propoi.
Optimality conditions for certain stochastic programming problems.
Autom. Remote Control 1971, 1210-1218 translation from Avtom.
Telemekh. 1971, No.8, 51-60 (1971)., 1972.
A.I. Kaplinskii, A.S. Poznjak, and A.I. Propoi.
Some methods for the solution of stochastic programming problems.
Autom. Remote Control 32, 1609-1616 translation from Avtom.
Telemekh. 1971, No.10, 87-94 (1971)., 1972.
A.I. Kaplinskii and A.I. Propoi.
Nonlocal optimization methods that use potential theory.
Avtomat. i Telemekh., 7:55-65, 1993.
A.I. Kaplinskii and A.I. Propoi.
Second-order refinement conditions in nonlocal optimization methods
that use potential theory.
Avtomat. i Telemekh., 8:104-117, 1994.
A.I. Kaplinskij.
Stochastic conditions for optimality.
In Issled. Operacii. Modeli, Sist. Resen. 2. 1970, 119-124,
1971.
A.I. Kaplinskij, A.S. Krasnenker, and A.V. Nazin.
Training of convolution principle in vector optimization problems.
Autom. Control Comput. Sci. 12, No.4, 41-45, 1978.
A.I. Kaplinskij, A.E. Limarev, and G.D. Chernyshova.
A construction of randomized optimization algorithms.
Probl. Sluchajnogo Poiska 8, 63-91, 1980.
A.I. Kaplinskij and A.M. Pesin.
A method of construction of randomized algorithms.
Autom. Remote Control 43, No.12, 1551-1559 translation from
Avtom. Telemekh. 1982, No.12, 65-75 (1982)., 1983.
A.I. Kaplinskij and A.I. Propoj.
Stochastic approach to nonlinear programming problems.
Automat. Remote Control 1970, 448-459 (970); translation from
Avtomat. Telemekh. 1970, No.3, 122-133, 1970.
Uday S. Karmarkar.
Convex/stochastic programming and multilocation inventory problems.
Naval Res. Logist. Quart., 26(1):1-19, 1979.
H.F. Karreman.
Duality in stochastic programming applied to the design and operation
of reservoirs.
In Recent results in stochastic programming (Proc. Meeting,
Oberwolfach, 1979), volume 179 of Lecture Notes in Econom. and Math.
Systems, pages 163-178. Springer, Berlin, 1980.
G.V. Karumidze and V.G. Thinvaleli.
Solution of a certain class of stochastic programming problems.
Sakharth. SSR Mecn. Akad. Moambe, 67:565-568, 1972.
A.N. Kashper and G.C. Varvaloucas.
Trunk implementation plan for hierarchical networks.
AT&T Bell Labs. Tech. J., 63(1):57-88, 1984.
E.I. Kasitskaya.
Approximation of the solution of a stochastic programming problem
with noise that is a homogeneous random field.
In Mathematical decision-making methods under conditions of
uncertainty (Russian), pages 23-27. Akad. Nauk Ukrain. SSR Inst. Kibernet.,
Kiev, 1990.
E.I. Kasitskaya and P.S. Knopov.
Asymptotic behaviour of empirical estimators in stochastic
programming problems.
Sov. Math., Dokl. 42, No.3, 751-753 translation from Dokl. Akad.
Nauk SSSR 315, No.2, 279-281 (1990)., 1991.
E.I. Kasitskaya and P.S. Knopov.
Convergence of empirical estimates in stochastic optimization
problems.
Kibernetika (Kiev), 2:104-107, 112, 135, 1991.
Aldona Katkauskait\.e.
Minimization algorithm in the presence of random noise.
Informatica, 1(1):59-70, 186, 197, 1990.
Aldona Katkauskait\.e.
Minimization of functions that are observable with noise.
Teor. Optimal. Reshenii, 14:78-92, 1990.
Aldona Katkauskat\.e.
An estimate of the parameters of a statistical model of global
optimization.
Teor. Optimal. Reshenii, 13:59-67, 1988.
V. Ja. Katkovnik.
The method of averaging operators in iteration algorithms for the
solution of stochastic extremal problems.
Kibernetika (Kiev), 4:123-131, 1972.
V. Ja. Katkovnik.
Iteration algorithms for random search in stochastic extremal
problems.
In Problems of random search, 2 (Russian), pages 31-42, 219.
Izdat. "Zinatne", Riga, 1973.
V. Ja. Katkovnik and L.V. Ovcarova.
Parametric statistical estimates in relay algorithms for random
search.
Avtomat. i Vycisl. Tehn. (Riga), 1:69-71, 1974.
V.Ja. Katkovnik and L.V. Ovcarova.
Konvergenz und Konvergenzgeschwindigkeit der Methode der
parametrischen statistischen Gradienten.
Kibernetika, Kiev 1974, Nr. 2, 115-118, 1974.
V.Ja. Katkovnik and L.V. Ovcarova.
Parametrical statistical evaluations in relay algorithms of random
search.
Avtomat. vycislit. Tehn., Riga 1974, Nr. 1, 69-71, 1974.
V.Ja. Katkovnik and L.V. Ovcarova.
Parametrische statistische Abschaetzungen in mehrstufigen
Algorithmen der zufaelligen Suche.
Kibernetika, Kiev 1974, Nr. 3, 94-100, 1974.
V.Ya. Katkovnik.
Numerical Methods for Solving Deterministic and Stochastic
Minimax Problems.
Naukova dumka, Kiev, 1979.
(in Russian).
V.Ya. Katkovnik and V.E. Khejsin.
Dynamic stochastic approximation of nonstationary solution of
constrained extremum problem.
Autom. Remote Control 41, 794-801 translation from Avtom.
Telemekh. 1980, No.6, 70-79 (1980)., 1980.
V.Ya. Katkovnik, O.Yu. Kul'chitskij, and V.E. Khejsin.
Approximation of solutions of strongly nonstationary stochastic
extremal problems in continuous time. I: Principles of design of algorithms.
Autom. Remote Control 43, 1424-1431 translation from Avtom.
Telemekh. 1982, No.11, 73-80 (1982)., 1983.
Naoki Katoh.
An e-approximation scheme for minimum variance problems.
J. Oper. Res. Soc. Japan, 33(1):46-65, 1990.
I. Ya. Kats and G.A. Timofeeva.
A bicriterial problem of stochastic optimization.
Avtomat. i Telemekh., 3:116-123, 1997.
Eliakim Katz.
A note on a comparative statics theorem for choice under risk.
J. Econ. Theory 25, 318-321, 1981.
A. Kaufmann and R. Cruon.
Strategies k-optimales dans les programmes dynamiques stochastiques
finies.
In Proc. 4th Int. Conf. Oper. Res., Boston 1966, 185-200,
1969.
Hemanshu Kaul and Sheldon H. Jacobson.
New global optima results for the Kauffman NK model: handling
dependency.
Math. Program., 108(2-3, Ser. B):475-494, 2006.
Michal Kaut and Stein W. Wallace.
Evaluation of scenario-generation methods for stochastic programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Michal Kaut and Stein W. Wallace.
Shape-based scenario generation using copulas.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
Michal Kaut and Stein W. Wallace.
Evaluation of scenario generation methods for stochastic programming.
Pac. J. Optim., 3(2):257-271, 2007.
Ivan Kavkler and Alenka Kavkler.
Stochastic linear optimization model (SLOM) using factor analysis.
In Proceedings of the 10th International Conference on
Operational Research-KOI 2004, pages 111-119. Univ. Osijek Dep. Math.,
Osijek, 2005.
V.V. Kazakevic and I.A. Mocalov.
Sequential algorithm for accelerated search for the extremum in
inertial optimization objects.
Dokl. Akad. Nauk SSSR, 226(1):77-80 (1 plate), 1976.
V.V. Kazakevich, I.A. Mochalov, and L.B. Prigozhin.
Optimization of measurements in accelerated algorithms of extremal
control.
Autom. Remote Control 47, 1217-1226 translation from Avtom.
Telemekh. 1986, No.9, 60-69 (1986)., 1986.
A. Kekhajov.
A minimum required number of statistical tests for one class of
simulation models.
Avtom. Izchislitelna Tekh. 15, No.2, 47-53, 1981.
P. Kelle.
Chance constrained inventory model for an asphalt mixing problem.
In Recent results in stochastic programming (Proc. Meeting,
Oberwolfach, 1979), volume 179 of Lecture Notes in Econom. and Math.
Systems, pages 179-189, Berlin, 1980. Springer.
Peter Kelle.
Stochastic programming models for safety stock allocation.
In Stochastic optimization, Proc. Int. Conf., Kiev/USSR 1984,
Lect. Notes Control Inf. Sci. 81, 402-411, 1986.
C.M. Keller, A.V. Cabot, and B.G. Flury.
Two algorithms for global optimization.
Math. Comput. Modelling, 21(12):47-59, 1995.
D.G. Kelly and J.W. Tolle.
Expected number of vertices of a random convex polyhedron.
SIAM J. Algebraic Discrete Methods, 2(4):441-451, 1981.
J. Kelman, J. R. Stedinger, L. A. Cooper, E. Hsu, and S-U. Yuan.
Sampling stochastic dynamic programming applied to reservoir
operations.
Water Resources Research, 26:447-454, 1990.
R. Kelman, L. A. N. Barroso, and M. V. F. Pereira.
Market power assessment and mitigation in hydrothermal systems.
IEEE Transactions on Power Systems, 16(3):354-359, 2001.
Milton L. Kelmanson and Priscila Goldenberg.
A convergent "small steps" feasible directions stochastic
approximation algorithm.
In Third Symposium on Operations Research (Univ. Mannheim,
Mannheim, 1978), Section 3, volume 33 of Operations Res. Verfahren,
pages 247-259. Hain, Königstein/Ts., 1979.
D.P. Kennedy.
Stimulating prices in a stochastic model of resource allocation.
Math. Oper. Res. 8, 151-157, 1983.
A.S. Kenyon and D.P. Morton.
Stochastic vehicle routing with random travel times.
Transportation Science, 37:69-82, 2003.
Astrid S. Kenyon and David P. Morton.
A survey on stochastic location and routing problems.
CEJOR Cent. Eur. J. Oper. Res., 9(4):277-328, 2001.
C. M. Kenyon, S. Savage, and B. Ball.
Equivalence of linear deviation about the mean and mean absolute
deviation about the mean objective functions.
Oper. Res. Lett., 24(4):181-185, 1999.
G. Keri.
On the two-stage programming under uncertainty.
Stud. Sci. Math. Hungar. 5, 37-40, 1970.
B. P. Kharlamov.
Semi-Markov processes for finding a maximum.
Avtomat. i Telemekh., (9):97-111, 2000.
Lov Kumar Kher and Soroosh Sorooshian.
A predictive demand model for systems planning, using noisy
realization theory.
Automatica 24, No.5, 671-676, 1988.
I.L. Khranovich.
Simulation of optimal development for water-supply systems. II: A
flow approach.
Autom. Remote Control 45, 1346-1353 translation from Avtom.
Telemekh. 1984, No.10, 121-130 (1984)., 1984.
V.N. Khrapko.
Full-step relaxation algorithms for stochastic programming.
In Dynamic systems, No. 2, pages 97-102. "Vishcha Shkola",
Kiev, 1983.
V.M. Kibardin.
Decomposition into functions in the minimization problem.
Automat. Remote Control, 40(9, part 1):1311-1323 (1980), 1979.
A. I. Kibzun and E. A. Kuznetsov.
Convex properties of the quantile function in stochastic programming
problems.
Avtomat. i Telemekh., (2):33-42, 2004.
A. I. Kibzun and I. V. Nikulin.
Discrete approximation of a linear two-stage stochastic programming
problem with a quantile criterion.
Avtomat. i Telemekh., (8):127-137, 2001.
A. I. Kibzun and G. L. Tretyakov.
On the smoothness of the criterial function in the quantile
optimization problem.
Avtomat. i Telemekh., 9:69-80, 1997.
A.I. Kibzun and Y.S. Kan.
Stochastic Programming Problems with Probability and Quantile
Functions.
Wiley, Chichester, 1996.
A.I. Kibzun and V.Yu. Kurbakovskij.
Guaranteeing approach to solving quantile optimization problems.
Ann. Oper. Res. 30, 81-93, 1991.
A.I. Kibzun, A.A. Lebedev, and V.V. Malyshev.
Reduction of an optimization problem with probabilistic constraints
to an equivalent minimax problem.
Soviet J. Comput. Systems Sci., 23(1):39-47, 1985.
A.I. Kibzun and V.V. Malyshev.
Generalized minimax approach to solving optimization problems with
chance constraints.
Engrg. Cybernetics, 22(1):105-114 (1985), 1984.
A.I. Kibzun and V.V. Malyshev.
Optimal control of a discrete-time stochastic system.
Sov. J. Comput. Syst. Sci. 24, No.2, 69-77 translation from Izv.
Akad. Nauk SSSR, Tekh. Kibern. 1985, No.6, 113-120 (1985)., 1986.
A.I. Kibzun and V.V. Malyshev.
Probabilistic optimization problems.
Sov. J. Comput. Syst. Sci. 28, No.4, 52-60 translation from Izv.
Akad. Nauk SSSR, Tekh. Kibern. 1989, No.6, 46-55 (1989)., 1990.
A.I. Kibzun and A.V. Naumov.
Two-stage problems of quantile linear programming.
Avtomat. i Telemekh., 1:83-93, 1995.
Andrey Kibzun and Riho Lepp.
Discrete approximation in quantile problem of portfolio selection.
In Stochastic optimization: algorithms and applications
(Gainesville, FL, 2000), volume 54 of Appl. Optim., pages 121-135.
Kluwer Acad. Publ., Dordrecht, 2001.
Masaaki Kijima and Masamitsu Ohnishi.
Stochastic orders and their applications in financial optimization.
Math. Methods Oper. Res., 50(2):351-372, 1999.
Financial optimization.
Masaaki Kijima and Akihisa Tamura.
On the greedy algorithm for stochastic optimization problems.
In Stochastic modelling in innovative manufacturing (Cambridge,
1995), volume 445 of Lecture Notes in Econom. and Math. Systems, pages
19-29. Springer, Berlin, 1997.
Hyong Sop Kim.
A computational procedure of the stochastic programming problem.
Cho-son In-min Kong-hwa-kuk Kwa-hak-won T'ong-bo,
4:39-43, 1982.
Joocheol Kim.
Estimation of optimality gap using stratified sampling.
Appl. Math. Comput., 171(2):710-720, 2005.
Tae-ho Kim and Soon Dal Park.
A decomposition method for two stage stochastic programming with
block diagonal structure.
J. Korean Oper. Res. Manage. Sci. Soc. 10, No.1, 9-13, 1985.
Yong Kim.
Convergence of a generalized gradient descent algorithm for limit
extremal problems.
Su-hak, 3:22-24, 1995.
A.J. King.
An implementation of the Lagrangian finite-generation method.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 295-311. Springer, Berlin, 1988.
A.J. King.
Stochastic programming problems: examples from the literature.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 543-567. Springer, Berlin, 1988.
A.J. King, editor.
Approximation and computation in stochastic programming.
North-Holland Publishing Co., Amsterdam, 1996.
Math. Programming 75 (1996), no. 2, Ser. B, see also Erratum in
Math. Programming 80 (1998), no. 1, Ser. A.
A.J. King, R.T. Rockafellar, L. Somlyody, and R.J.-B. Wets.
Lake eutrophication management: The Lake Balaton project.
In Numerical techniques for stochastic optimization, Springer
Ser. Comput. Math. 10, 435-444, 1988.
A.J. King and R.J.-B. Wets.
Erratum: "Epi-consistency of convex stochastic programs".
Stochastics Stochastics Rep., 37(4):259-260, 1991.
Alan King and Lisa Korf.
Martingale pricing measures in incomplete markets via stochastic
programming duality in the dual of L¥.
Stochastic Programming E-Print Series, http://www.speps.org,
2001.
Alan J. King.
Asymmetric risk measures and tracking models for portfolio
optimization under uncertainty.
Ann. Oper. Res. 45, No.1-4, 165-177, 1993.
Alan J. King.
Duality and martingales: a stochastic programming perspective on
contingent claims.
Math. Program., 91(3, Ser. B):543-562, 2002.
ISMP 2000, Part 1 (Atlanta, GA).
Alan J. King, Matti Koivu, and Teemu Pennanen.
Calibrated option bounds.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Alan J. King and R. Tyrrell Rockafellar.
Asymptotic theory for solutions in statistical estimation and
stochastic programming.
Math. Oper. Res., 18(1):148-162, 1993.
Alan J. King, László Somlyódy, and Roger J.-B. Wets.
Stochastic optimization for lake eutrophication management.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 347-378. SIAM, Philadelphia, PA, 2005.
Alan J. King, Samer Takriti, and Shabbir Ahmed.
Issues in risk modeling for multi-stage systems.
Ibm research report rc 20993, IBM Research Division, T.J. Watson
Research Center, Yorktown Heights, New York, 1997.
Alan J. King and Roger J.-B. Wets.
Epi-consistency of convex stochastic programs.
Stochastics Stochastics Rep., 34(1-2):83-92, 1991.
Alan J. King, Stephen E. Wright, Gyana R. Parija, and Robert Entriken.
The IBM stochastic programming system.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 21-36. SIAM, Philadelphia, PA, 2005.
M.J.L. Kirby.
The current state of chance-constrained programming.
In Proceedings of the Princeton Symposium on Mathematical
Programming (Princeton, 1967), pages 93-111, Princeton, N.J., 1970.
Princeton Univ. Press.
Scott Kirkpatrick.
Optimization by simulated annealing: quantitative studies.
J. Statist. Phys., 34(5-6):975-986, 1984.
V.P. Kirlitsa.
On a stochastic programming problem with linear decision rules.
Vestn. Beloruss. Gos. Univ. Im. V. I. Lenina, Ser. I 1983, No.1,
35-37, 1983.
V.P. Kirlitsa.
A problem of linear stochastic programming with two-sided
constraints.
Vestnik Beloruss. Gos. Univ. Ser. I, 1:33-36, 79, 1985.
V.P. Kirlitsa and A. Casado.
On a problem of stochastic linear programming with nondeterministic
constraint matrix.
Investigación Oper., 27:3-8, 1979.
V.P. Kirlitsa and Rosa Vázquez.
On a problem of stochastic linear programming with two-sided
constraints.
Investigación Oper., 27:9-16, 1979.
V.P. Kirlitza and Alfonso Casado Collado.
On a problem of stochastic linear programming.
Investigación Oper., 1(1):27-33, 1980.
V.P. Kirlitza and Alfonso Casado Collado.
On the problem of linear stochastic programming with the uniform
distribution.
Investigación Oper., 3(2):41-50, 1982.
N.I. Kiselev.
Linear programming in extremal problems of statistics.
In Statistics. Probability. Economics, volume 49 of Uchen.
Zap. Statist., pages 31-39. "Nauka", Moscow, 1985.
V.G. Kiselev.
Approximation of probability constraints in some production planning
problems.
U.S.S.R. Comput. Math. Math. Phys. 23, No.1, 56-63 translation
from Zh. Vychisl. Mat. Mat. Fiz. 23, No.1, 83-94 (1983)., 1983.
E. M. Kiseleva and K. A. Kuznetsov.
On the solution of the continuous stochastic problem of optimal
partitioning with reconstruction of the objective function.
Problemy Upravlen. Inform., 151(6):81-88, 1998.
E.M. Kiseleva and N.Z. Shor.
The solution of the continuous optimal decomposition problem with
incomplete initial data.
Comput. Math. Math. Phys. 31, No.6, 13-20 translation from Zh.
Vychisl. Mat. Mat. Fiz. 31, No.6, 799-809 (1991)., 1991.
K.C. Kiwiel, C.H. Rosa, and A. Ruszczy\'nski.
Decomposition via alternating linearization.
Working paper WP-95-051, IIASA, Laxenburg, 1995.
Krzysztof C. Kiwiel.
Proximal minimization methods with generalized Bregman functions.
SIAM J. Control Optim., 35(4):1142-1168, 1997.
Krzysztof C. Kiwiel, Charles H. Rosa, and Andrzej Ruszczy\'nski.
Proximal decomposition via alternating linearization.
SIAM J. Optim., 9(3):668-689, 1999.
G. Kjellstroem and L. Taxen.
Gaussian adaptation, an evolution-based efficient global optimizer.
In Computational and applied mathematics, I. Algorithms and
theory, Sel. Rev. Pap. IMACS 13th World Congr., Dublin/Irel. 1991, 267-276,
1992.
Gregor Kjellström and Lars Taxén.
Stochastic optimization in system design.
IEEE Trans. Circuits and Systems, 28(7):702-715, 1981.
Hans-Siegfried Klausmann.
Stochastische Entscheidungsbäume.
Verlag Anton Hain, Meisenheim am Glan, 1976.
Ein Beitrag zur Lösung von sequentiellen Entscheidungsproblemen bei
Risiko, Mit einem Vorwort von M. Meyer, Beiträge zur Datenverarbeitung und
Unternehmensforschung, Band 18.
G. Klein, H. Moskowitz, and A. Ravindran.
Interactive multiobjective optimization under uncertainty.
Management Sci., 36(1):58-75, 1990.
Willem K. Klein Haneveld.
Duality in stochastic linear and dynamic programming, volume
274 of Lecture Notes in Economics and Mathematical Systems.
Springer-Verlag, Berlin, 1986.
Willem K. Klein Haneveld.
Robustness against dependence in PERT: An application of duality and
distributions with known marginals.
Math. Program. Study 27, 153-182, 1986.
Willem K. Klein Haneveld, Leen Stougie, and Maarten H. van der Vlerk.
On the convex hull of the simple integer recourse objective function.
Ann. Oper. Res., 56:209-224, 1995.
Stochastic programming (Udine, 1992).
Willem K. Klein Haneveld, Leen Stougie, and Maarten H. van der Vlerk.
An algorithm for the construction of convex hulls in simple integer
recourse programming.
Ann. Oper. Res., 64:67-81, 1996.
Stochastic programming, algorithms and models (Lillehammer, 1994).
Willem K. Klein Haneveld, Leen Stougie, and Maarten H. van der Vlerk.
Simple integer recourse models.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Willem K. Klein Haneveld, Leen Stougie, and Maarten H. van der Vlerk.
Simple integer recourse models: convexity and convex approximations.
Math. Program., 108(2-3, Ser. B):435-473, 2006.
Willem K. Klein Haneveld and Maarten H. van der Vlerk.
On the expected value function of a simple integer recourse problem
with random technology matrix.
J. Comput. Appl. Math., 56(1-2):45-53, 1994.
Stochastic programming: stability, numerical methods and applications
(Gosen, 1992).
Willem K. Klein Haneveld and Maarten H. van der Vlerk.
Stochastic integer programming: general models and algorithms.
Ann. Oper. Res., 85:39-57, 1999.
Stochastic programming. State of the art, 1998 (Vancouver, BC).
Willem K. Klein Haneveld and Maarten H. van der Vlerk.
Optimizing electricity distribution using two-stage integer recourse
models.
In Stochastic optimization: algorithms and applications
(Gainesville, FL, 2000), volume 54 of Appl. Optim., pages 137-154.
Kluwer Acad. Publ., Dordrecht, 2001.
Willem K. Klein Haneveld and Maarten H. van der Vlerk.
Integrated chance constraints: reduced forms and an algorithm.
Comput. Manag. Sci., 3(4):245-269, 2006.
W.K. Klein Haneveld.
Some linear programs in probabilities and their duals.
In Convexity and duality in optimization (Groningen, 1984),
volume 256 of Lecture Notes in Econom. and Math. Systems, pages
95-141, Berlin, 1985. Springer.
W.K. Klein Haneveld.
On integrated chance constraints.
In Stochastic programming (Gargnano, 1983), volume 76 of
Lecture Notes in Control and Inform. Sci., pages 194-209. Springer, Berlin,
1986.
W.K. Klein Haneveld, L. Stougie, and M.H. van der Vlerk.
Stochastic integer programming with simple recourse.
Research Memorandum 455, Institute of Economic Research, University
of Groningen, 1991.
W.K. Klein Haneveld, L. Stougie, and M.H. van der Vlerk.
On the convex hull of the composition of a separable and a linear
function.
Discussion Paper 9570, CORE, Louvain-la-Neuve, Belgium, 1995.
W.K. Klein Haneveld, L. Stougie, and M.H. van der Vlerk.
Convex approximations for simple integer recourse models by
perturbing the underlying distribution.
Research Report 97A19, SOM, University of Groningen, 1997.
W.K. Klein Haneveld, L. Stougie, and M.H. van der Vlerk.
Convex simple integer recourse models.
Research Report 97A10, SOM, University of Groningen, 1997.
W.K. Klein Haneveld, M.H. Streutker, and M.H. van der Vlerk.
An ALM model for pension funds using integrated chance constraints.
Research Report 05A03, SOM, University of Groningen, 2005.
Revision of 03A21.
W.K. Klein Haneveld, M.H. Streutker, and M.H. van der Vlerk.
Modeling ALM for dutch pension funds.
In J. Safrankova and J. Pavlu, editors, WDS'06 Proceedings of
Contributed Papers: Part I - Mathematics and Computer Sciences, pages
100-105, Prague, 2006. Matfyzpress.
W.K. Klein Haneveld and M.H. van der Vlerk.
Optimizing electricity distribution using two-stage integer recourse
models.
In S. Uryasev and P.M. Pardalos, editors, Stochastic
Optimization: Algorithms and Applications, pages 137-154. Kluwer Academic
Publishers, 2001.
W.K. Klein Haneveld and M.H. van der Vlerk.
Stochastic Programming (Lecture Notes).
Available on request, e-mail m.h.van.der.vlerk@rug.nl, 2007.
N. L. Kleinman.
Stochastic approximation algorithms: theory and applications.
Ph.D. thesis, Dept. of Mathematical Sciences, The Johns Hopkins
University, 1996.
N.L. Kleinman, S.D. Hill, and V.A. Ilenda.
SPSA/SIMMOD optimization of air traffic delay cost.
In Proceedings of the American Control Conference, pages
1121-1125, 1997.
N.L. Kleinman, J.C. Spall, and D.Q. Naiman.
Simulation-based optimization with stochastic approximation using
common random numbers.
Management Science, 45:1570-1578, 1999.
Anton J. Kleywegt and Jason D. Papastavrou.
The dynamic and stochastic knapsack problem.
Oper. Res., 46(1):17-35, 1998.
Anton J. Kleywegt and Jason D. Papastavrou.
The dynamic and stochastic knapsack problem with random sized items.
Oper. Res., 49(1):26-41, 2001.
Anton J. Kleywegt, Alexander Shapiro, and Tito Homem-de Mello.
The sample average approximation method for stochastic discrete
optimization.
SIAM J. Optim., 12(2):479-502 (electronic), 2001/02.
Allen Klinger and O.L. Mangasarian.
Logarithmic convexity and geometric programming.
J. math. Analysis Appl. 24, 388-408, 1968.
Rolf Kloetzler.
Dualitaet und Fehlerabschaetzungen bei stochastischer diskreter
dynamischer Optimierung.
Seminarber., Humboldt-Univ. Berlin, Sekt. Math. 39, 114-124,
1981.
Leszek Klukowski.
Some probabilistic properties of the nearest adjoining order method
and its extensions.
Ann. Oper. Res., 51:241-261, 1994.
Support for decision and negotiation processes (Warsaw, 1992).
Leonhard Knauff.
Ein Programm zur Parameterschätzung in nichtlinearen
Modellen.
Wiss. Z. Martin-Luther-Univ. Halle-Wittenberg Math.-Natur.
Reihe, 34(1):149-152, 1985.
Charles S. Knode and Lloyd A. Swanson.
A stochastic model for bidding.
J. Oper. Res. Soc. 29, 951-957, 1978.
P. S. Knopov and E. I. Kasitskaya.
On large deviations of empirical estimates in stochastic programming
problems.
Kibernet. Sistem. Anal., 40(4):52-60, 189, 2004.
P. S. Knopov and E. I. Kasitskaya.
On the convergence of empirical estimates in stochastic programming
problems in discrete-time processes.
Kibernet. Sistem. Anal., 41(1):175-178, 191, 2005.
Pavel S. Knopov and Evgeniya J. Kasitskaya.
Empirical estimates in stochastic optimization and
identification, volume 71 of Applied Optimization.
Kluwer Academic Publishers, Dordrecht, 2002.
P.S. Knopov.
Remarks on the minimization of nondifferentiable functions.
Kibernetika (Kiev), 2:44-45, 1975.
P.S. Knopov.
Weak convergence of measures generated by iterative procedures in
Hilbert space.
Dokl. Akad. Nauk SSSR, 256(1):29-32, 1981.
P.S. Knopov.
An approach to solving problems of stochastic optimization.
Kibernetika (Kiev), 4:126-127, 136, 1988.
P.S. Knopov and E.J. Kasitskaya.
Properties of empirical estimates in stochastic optimization and
identification problems.
Ann. Oper. Res., 56:225-239, 1995.
Stochastic programming (Udine, 1992).
M. Knott.
Randomized decisions in chance-constrained programming.
J. Oper. Res. Soc. 36, 959-961, 1985.
M.I. Koch, D.C. Chin, and R.H. Smith.
Network-wide approach to optimal signal light timing for integrated
transit vehicle and traffic operations.
In Proceedings of the 7th National Conference on Light Rail
Transit, volume 2, pages 126-131. National Academy of Sciences Press, 1997.
A.I. Kochubinskii.
Recognition of pulse packets under conditions of incomplete
information.
Dokl. Akad. Nauk Ukrain. SSR Ser. A, 12:56-58, 86, 1988.
U. Kockelkorn.
Ein linearen Mehrentscheidungsmodell.
Metrika 26, 169-182, 1979.
Gerald Koenke.
Lineare und stochastische Optimierung mit dem PC. (Linear and
stochastic optima with the PC).
Mikro-Computer-Praxis. Stuttgart: B. G. Teubner. 157 S., 1987.
Konstantin Kogan, Eugene Khmelnitsky, and Toshihide Ibaraki.
Dynamic generalized assignment problems with stochastic demands and
multiple agent-task relationships.
J. Global Optim., 31(1):17-43, 2005.
Matti Koivu.
Variance reduction in sample approximations of stochastic programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Matti Koivu.
Variance reduction in sample approximations of stochastic programs.
Math. Program., 103(3, Ser. A):463-485, 2005.
M.A. Kokorev.
Construction of a stochastic algorithm, optimal for one step, for the
approximation of a Lipschitz function.
Zh. Vychisl. Mat. i Mat. Fiz., 30(5):652-662, 798, 1990.
V.V. Kolbin.
Stochastic programming.
In Progress Math. 11, 1-75, 1971.
V.V. Kolbin.
Stochastic programming.
Theory and Decision Library. Vol. 14. Dordrecht - Boston: D. Reidel
Publishing Company, 1977.
Translated from Russian by Igor P. Grigoryev.
Waldemar Kolodziejczyk.
On equivalence of two optimization methods for fuzzy discrete
programming problems.
Eur. J. Oper. Res. 36, No.1, 85-91, 1988.
Michael Kolonko.
Uniform bounds for a dynamic programming model under adaptive control
using exponentially bounded error probabilities.
In Mathematical learning models-theory and algorithms (Bad
Honnef, 1982), volume 20 of Lecture Notes in Statist., pages 108-114.
Springer, New York, 1983.
E. Komaromi.
A dual approach to stochastic linear programming problems
constrained by logarithmic concave joint probability distribution function.
Alkalmazott Mat. Lapok 9, 85-92, 1983.
É. Komáromi.
A dual method for probabilistic constrained problems.
Math. Programming Stud., 28:94-112, 1986.
Stochastic programming 84. II.
É. Komáromi.
On properties of the probabilistic constrained linear programming
problem and its dual.
J. Optim. Theory Appl., 55(3):377-390, 1987.
É. Komáromi.
Probabilistic constraints in primal and dual linear programs: duality
results.
J. Optim. Theory Appl., 75(3):587-602, 1992.
Éva Komáromi.
Actual approach to stochastic linear programming problems constrained
by logarithmic concave joint probability distribution function.
Alkalmaz. Mat. Lapok, 9(1-2):85-92, 1983.
Éva Komáromi.
Duality in probabilistic constrained linear programming.
In System modelling and optimization (Budapest, 1985),
volume 84 of Lecture Notes in Control and Inform. Sci., pages 423-429.
Springer, Berlin, 1986.
Éva Komáromi.
On the convergence of a dual algorithm for the solution of the
probabilistic constrained linear programming problem.
Alkalmaz. Mat. Lapok, 14(1-2):85-98, 1989.
S.V. Komolov, S.P. Makeev, G.P. Serov, and I.F. Shakhnov.
Optimal control of a finite automaton with fuzzy constraints and a
fuzzy target.
Cybernetics, 15(6):805-810 (1980), 1979.
Nan Kong, Andrew Schaefer, and Shabbir Ahmed.
Totally unimodular stochastic programs.
Optimization Online, http://www.optimization-online.org, 2006.
Nan Kong and Andrew J. Schaefer.
A factor 1/2 approximation algorithm for a class of two-stage
stochastic mixed-integer programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Nan Kong and Andrew J. Schaefer.
A factor 1/2 approximation algorithm for two-stage
stochastic matching problems.
European J. Oper. Res., 172(3):740-746, 2006.
Nan Kong, Andrew J. Schaefer, and Brady Hunsaker.
Two-stage integer programs with stochastic right-hand sides.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Ger Koole.
Stochastic scheduling with event-based dynamic programming.
Math. Methods Oper. Res., 51(2):249-261, 2000.
Charles Kooperberg and Charles J. Stone.
Stochastic optimization methods for fitting polyclass and
feed-forward neural network models.
J. Comput. Graph. Statist., 8(2):169-189, 1999.
Jozef Kopec and Bogdan Krawiec.
Application of model "P" in a plant production optimization
problem.
Przegl. Stat. 29, 551-558, 1982.
Lisa A. Korf.
A finite-dimensional approach to infinite-dimensional constraints in
stochastic programming duality.
In Stochastic optimization: algorithms and applications
(Gainesville, FL, 2000), pages 155-167. Kluwer Acad. Publ., Dordrecht,
2001.
Lisa A. Korf.
Stochastic programming duality: L¥ multipliers with
an application to mathematical finance.
Stochastic Programming E-Print Series, http://www.speps.org,
2001.
Lisa A. Korf.
Stochastic programming duality: L\sp ¥ multipliers
for unbounded constraints with an application to mathematical finance.
Math. Program., 99(2, Ser. A):241-259, 2004.
Lisa A. Korf and Roger J.-B. Wets.
An ergodic theorem for stochastic programming problems.
In Optimization (Namur, 1998), pages 203-217. Springer,
Berlin, 2000.
Lisa A. Korf and Roger J.-B. Wets.
Random lsc functions: An ergodic theorem.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Lisa A. Korf and Roger J.-B. Wets.
Random lsc functions: Scalarization.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Lisa A. Korf and Roger J.-B. Wets.
Random-lsc functions: an ergodic theorem.
Math. Oper. Res., 26(2):421-445, 2001.
A.S. Korkhin.
Some properties of estimates for regression parameters with a priori
inequality constraints.
Kibernetika (Kiev), 6:iv, 106-114, 1985.
J.S.H. Kornbluth.
Duality, indifference and sensitivity analysis in multiple objective
linear programming.
Operat. Res. Quart. 25, 599-614, 1974.
J. Koronacki.
On the convergence of minimization algorithms in problems described
by stochastic equations.
In 18. internat. wiss. Kolloqu., Ilmenau 1, 51-53, 1973.
Ja. Koronacki.
The convergence of random search algorithms.
Avtomat. i Vycisl. Tehn. (Riga), 4:43-49, 92, 1976.
Jacek Koronacki.
A stochastic approximation counterpart of the feasible direction
method.
Statist. Probab. Lett., 5(6):415-419, 1987.
A.P. Korostelev.
Multistep procedures of stochastic optimization.
Autom. Remote Control 42, No.5, 621-628 translation from Avtom.
Telemekh. 1981, No.5, 82-90 (1981)., 1981.
A.P. Korostelev and E.A. Kitaev.
Comparison of asymptotic behaviour of stochastic approximation and
random search procedures.
Avtom. Vychisl. Tekh. 1977, No.6, 37-38, 1977.
K.O. Kortanek.
Semi-infinite programming duality for order restricted statistical
inference models.
Z. Oper. Res., 37(3):285-301, 1993.
J. Korycki.
On a distributed implementation of a decomposition method for
multistage linear stochastic programs.
Optimization, 38(2):173-200, 1996.
Bernd Kost.
Structural design via evolution strategies.
In Stochastic programming (Neubiberg/München, 1993), volume
423 of Lecture Notes in Econom. and Math. Systems, pages 71-92.
Springer, Berlin, 1995.
L.P. Kostina.
A method for solving the optimal resource allocation problem on
stochastic networks with a complex space-time structure.
Vestnik S.-Peterburg. Univ. Mat. Mekh. Astronom., 3:36-43,
116-117, 1992.
Vassilis S. Kouikoglou and Yannis A. Phillis.
Minimax design of two-state k-out-of-n systems.
Appl. Stochastic Models Data Anal., 9(3):245-250, 1993.
R. Kouwenberg.
Scenario generation and stochastic programming models for asset
liability management.
European Journal of Operational Research, 134(2):279-292,
2001.
I.N. Kovalenko.
Probabilistic Calculation and Optimization.
Naukova dumka, Kiev, 1989.
(in Russian).
I.N. Kovalenko, N.Yu. Kuznetsov, and A.N. Nakonechnyj.
Optimization of the characteristics of the reliability of systems on
the basis of using qualitative estimates of continuity and methods of
accelerate modeling.
In Zolotarev, V. M. (ed.) et al., Stability problems of
stochastic models. Seminar proceedings, Sukhumi (USSR), October 1987. Moskva:
Vsesoyuznyj Nauchno-Issledovatel'skij Institut Sistemnykh Issledovanij,
Probl. Ustojch. Stokhasticheskikh Modelej, Tr. Semin. 79-84, 1988.
I.N. Kovalenko and A.N. Nakonechnyj.
Approximate calculation and optimization of reliability.
(Priblizhennyj raschet i optimizatsiya nadezhnosti).
Naukova Dumka, Kiev, 1989.
M.M. Kovalev and V.A. Pir'yanovich.
Locally stochastic algorithms of discrete optimization (experiments
and computational experience).
Cybernetics, 18(1):127-132, 1982.
S.V. Kovalev and V.V. Mazalov.
A risk function in global optimization problem.
In Probabilistic methods in discrete mathematics (Petrozavodsk,
1992), volume 1 of Progr. Pure Appl. Discrete Math., pages 266-270.
VSP, Utrecht, 1993.
S.V. Kovalev and V.V. Mazalov.
The risk function in the problem of the design of an experiment.
In Modeling of natural systems and optimal control problems
(Russian) (Chita), pages 83-92. VO "Nauka", Novosibirsk, 1993.
P. Kovanic and R.A. Barack.
Robust survival model as an optimization problem.
In System modelling and optimization (Compiègne, 1993),
volume 197 of Lecture Notes in Control and Inform. Sci., pages
375-382. Springer, London, 1994.
Andrzej Koziarski.
Application of the stochastic subgradient method for long-term
planning of the state of a dynamic plant operating in the presence of random
disturbances.
Arch. Automat. Telemech., 25(4):507-517 (1981), 1980.
O. Krafft.
Dual optimization problems in stochastics.
Jahresber. Deutsch. Math.-Verein., 83(3):97-105, 1981.
W.F Krajewski.
The sequential problem under uncertainty. The development of water
systems.
Comput. Math. Appl, 8:313-318, 1982.
A.S. Krasnenker.
The method of local improvements in the vector-optimization
problem.
Autom. Remote Control 36, 419-422 translation from Avtom.
Telemekh. 1975, No.3, 75-79 (1975)., 1975.
A.A. Krasovskii.
Continuous algorithms and the stochastic dynamics of searching for an
extremum.
Avtomat. i Telemekh., 4:55-65, 1991.
See also Erratum, ibid. 10:189, 1991.
A.N. Krasovskij.
Control under minimax of an integral functional.
Sov. Math., Dokl. 44, No.2, 525-528 translation from Dokl. Akad.
Nauk SSSR 320, No.4, 785-788 (1991)., 1992.
N.N. Krasovskij.
A minimax control problem.
Differ. Equations 18, 1511-1517 translation from Differ. Uravn.
18, No.12, 2126-2132 (1982)., 1983.
N.N. Krasovskij.
Control with deficit of information.
Sov. Math., Dokl. 31, 108-112 translation from Dokl. Akad. Nauk
SSSR 280, 536-540 (1985)., 1985.
N.N. Krasovskij and V.E. Tret'yakov.
A stochastic program synthesis of a guaranteeing control.
Probl. Control Inf. Theory 12, 79-95 (Russian. English
translation enclosed), 1983.
L.V. Kravtsova, Ya. D. Plotkin, and L.G. Smyshlyaeva.
On the theory of optimization with respect to a probability criterion
and its use in calculating the output percentage of nondefective
semiconductor devices.
In Partial differential equations (Russian), pages 70-79.
"Obrazovanie", St. Petersburg, 1992.
Jacek B. Krawczyk.
On variance constrained programming.
Asia-Pacific J. Oper. Res., 7(2):190-208, 1990.
L.I. Krechetov.
Necessary conditions of optimality with constraints which are valid
"almost everywhere" with respect to a controlled measure.
In Studies in stochastic optimization and mathematical
economics, Moskva, 89- 130, 1986.
Christian Kredler.
An SQP-method for linearly constrained maximum likelihood
problems.
In Applied mathematics and parallel computing, pages 157-174.
Physica, Heidelberg, 1996.
J. Kreimer.
Generalized estimates for performance sensitivities of stochastic
systems.
Math. Comput. Modelling 10, No.12, 911-922, 1988.
J. Kreimer and R.Y. Rubinstein.
Smoothed functionals and constrained stochastic approximation.
SIAM J. Numer. Anal., 25(2):470-487, 1988.
Moshe Kress.
The chance constrained critical path with location-scale
distributions.
Eur. J. Oper. Res. 18, 359-363, 1984.
Ortwin Kreutzberger.
Bemerkungen zur Quotienten- und Produktoptimierung mit Anwendung
beim Risikoproblem der stochastischen Optimierung.
Wiss. Z. Martin-Luther Univ. Halle-Wittenberg, Math.-naturw. R.
27, No.3, 75-79, 1978.
Ortwin Kreutzberger and Lothar Kuegler.
Bestimmung von Stabilitaetsbereichen in der stochastischen linearen
Optimierung mit Hilfe parametrischer Optimierung und Regressionsrechnung.
Wiss. Z., Martin-Luther-Univ. Halle-Wittenberg, Math.-Naturwiss.
Reihe 26, No.5, 71-79, 1977.
A. Krishnamoorthy and P. V. Ushakumari.
k-out-of-n G system with repair: the D-policy.
Comput. Oper. Res., 28(10):973-981, 2001.
Trine Krogh Kristoffersen.
Deviation measures in linear two-stage stochastic programming.
Math. Methods Oper. Res., 62(2):255-274, 2005.
Nikolai Krivulin.
An analysis of gradient estimates in stochastic network optimization
problems.
In Model-oriented data analysis (Petrodvorets, 1992), Contrib.
Statist., pages 227-240. Physica, Heidelberg, 1993.
Nikolai Krivulin.
Unbiased estimates for gradients of stochastic network performance
measures.
Acta Appl. Math., 33(1):21-43, 1993.
Stochastic optimization.
P. Krokhmal, S. Uryasev, and G. Zrazhevsky.
Numerical comparison of conditional value-at-risk and conditional
drawdown-at-risk approaches: application to hedge funds.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 609-631. SIAM, Philadelphia, PA, 2005.
Pavlo Krokhmal, Robert Murphey, Panos Pardalos, and Stanislav Uryasev.
Use of conditional value-at-risk in stochastic programs with poorly
defined distributions.
In Recent developments in cooperative control and optimization,
volume 3 of Coop. Syst., pages 225-241. Kluwer Acad. Publ., Boston,
MA, 2004.
Pavlo A. Krokhmal and Robert Murphey.
Modeling and implementation of risk-averse preferences in stochastic
programs using risk measures.
In Robust optimization-directed design, volume 81 of
Nonconvex Optim. Appl., pages 95-116. Springer, New York, 2006.
Piotr Krysta and Roberto Solis-Oba.
Approximation algorithms for bounded facility location problems.
J. Comb. Optim., 5(2):233-247, 2001.
Mikio Kubo and Hiroshi Kasugai.
Randomized decision strategy for the hierarchical optimization
problems.
J. Oper. Res. Soc. Japan 33, No.4, 335-353, 1990.
Christian Küchler and Stefan Vigerske.
Decomposition of multistage stochastic programs with recombining
scenraio trees.
Stochastic Programming E-Print Series, http://www.speps.org,
2007.
A. Kucia and A. Nowak.
On e-optimal continuous selectors and their application in
discounted dynamic programming.
J. Optimization Theory Appl. 54, 289-302, 1987.
V. ¯I. Kud¯in and G. ¯I. Kud¯in.
Application of the small parameter method to the investigation of
weakly nonlinear systems. II.
V¯isn. Kiïv. Un¯iv. Ser. F¯iz.-Mat. Nauki,
(3):252-256, 2000.
Daniel Kuhn.
Aggregation and discretization in multistage stochastic programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Daniel Kuhn.
Generalized bounds for convex multistage stochastic programs,
volume 548 of Lecture Notes in Economics and Mathematical Systems.
Springer-Verlag, Berlin, 2005.
Daniel Kuhn.
Convergent bounds for stochastic programs with expected value
constraints.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
O.Yu. Kul'chitskij.
Non-Markov algorithms for statistical optimization with continuous
time. I.
Autom. Remote Control 39, 680-691 translation from Avtom.
Telemekh. 1978, No.5, 73-86 (1978)., 1978.
O.Yu. Kul'chitskij.
Non-Markovian algorithms for statistical optimization with
continuous time. II.
Autom. Remote Control 39, 823-832 translation from Avtom.
Telemekh. 1978, No.6, 56-66 (1978)., 1978.
O.Yu. Kul'chitskij.
Sufficient conditions for convergence of stochastic approximation
algorithms for random processes with continuous time.
Cybernetics 15, 901-917 translation from Kibernetika 1979, No.6,
114-126 (1979)., 1980.
O.Yu. Kul'chitskij.
Accelerated convergence effect in stochastic programming algorithms
with correlated noise.
Autom. Remote Control 47, 375-379 translation from Avtom.
Telemekh. 1986, No.3, 94-99 (1986)., 1986.
Rudolf Kulhavý.
Recursive nonlinear estimation, volume 216 of Lecture
Notes in Control and Information Sciences.
Springer-Verlag London Ltd., London, 1996.
A geometric approach.
V.G. Kulkarni and J.S. Provan.
An improved implementation of conditional Monte Carlo estimation of
path lengths in stochastic networks.
Oper. Res. 33, 1389-1393, 1985.
Vidyadhar G. Kulkarni.
Modeling and Analysis of Stochastic Systems.
Texts in Statistical Science Series. Chapman and Hall Ltd., London,
1995.
S. Kumar.
Chance-constrained programming problem with chance-constrained
relative lower bounded variables.
Trabajos Estadíst. Investigación Oper., 30(2):73-77,
1979.
P. L. Kunsch and J. Teghem.
Nuclear-fuel cycle optimization using multiobjective stochastic
linear programming.
European Journal of Operational Research, 31(2):240-249, 1987.
P.L. Kunsch.
Application of "STRANGE" to energy studies.
In Stochastic versus fuzzy approaches to multiobjective
mathematical programming under uncertainty, volume 6 of Theory Decis.
Lib. Ser. D System Theory Knowledge Engrg. Probl. Solving, pages 117-130.
Kluwer Acad. Publ., Dordrecht, 1990.
Alexandra Künzi-Bay and János Mayer.
Computational aspects of minimizing conditional value-at-risk.
Comput. Manag. Sci., 3(1):3-27, 2006.
V. Yu. Kurbakovskii.
A numerical method for solving an inverse probability problem.
In Analysis and design of dynamical systems under conditions of
uncertainty (Russian), pages 11-18. Moskov. Aviatsion. Inst., Moscow, 1990.
Yasuaki Kurokawa and Kenjiro Nakamura.
Forest management planning under uncertainty.
J. Operations Res. Soc. Japan 20, 259-272, 1977.
Harold J. Kushner.
Necessary conditions for discrete parameter stochastic optimization
problems.
In Proceedings of the Sixth Berkeley Symposium on Mathematical
Statistics and Probability (Univ. California, Berkeley, Calif., 1970/1971),
Vol. III: Probability theory, pages 667-685, Berkeley, Calif., 1972. Univ.
California Press.
Harold J. Kushner.
Stochastic approximation algorithms for constrained optimization
problems.
Ann. Statist., 2:713-723, 1974.
Harold J. Kushner.
Rates of convergence for sequential Monte Carlo optimization
methods.
SIAM J. Control Optim., 16(1):150-168, 1978.
Harold J. Kushner.
Approximation methods for the minimum average cost per unit time
problem with a diffusion model.
In Approximate solution of random equations, pages 107-126,
New York, 1979. North-Holland.
Harold J. Kushner and Milton L. Kelmanson.
Stochastic approximation algorithms of the multiplier type for the
sequential Monte Carlo optimization of stochastic systems.
SIAM J. Control Optimization, 14(5):827-842, 1976.
Harold J. Kushner and Jichuan Yang.
A Monte Carlo method for sensitivity analysis and parametric
optimization of nonlinear stochastic systems: the ergodic case.
SIAM J. Control Optim., 30(2):440-464, 1992.
H.J. Kushner and D.S. Clark.
A stochastic approximation projection algorithm for constrained
Monte- Carlo optimization.
In A link between science and applications of automatic
control, Proc. 7th trienn. World Congr., Helsinki 1978, Vol. 4, 2425-2433,
1979.
V.S. Kuzin.
Analytical solution of the probabilities of an optimization problem
when there are rigid constraints.
Soviet J. Comput. Systems Sci., 30(4):125-130, 1992.
V.A. Labkovskii.
Control under an independent change of the nonobservable parameter.
Kibernetika (Kiev), 2:51-57, 133, 1987.
Petr Lachout.
On multifunction transforms of probability measures.
Ann. Oper. Res., 56:241-249, 1995.
Stochastic programming (Udine, 1992).
R.R. Laferriere and S.M. Robinson.
Scenario analysis in u.s. army decision making.
Phalanx, 33(1):10ff, 2000.
J.C. Lagarias and F. Aminzadeh.
Multi-stage planning and the extended linear-quadratic-Gaussian
control problem.
Math. Oper. Res. 8, 42-61, 1983.
B.J. Lageweg, J.K. Lenstra, A.H.G. Rinnooy Kan, and L. Stougie.
Stochastic integer programming by dynamic programming.
Stat. Neerl. 39, 97-113, 1985.
B.J. Lageweg, J.K. Lenstra, A.H.G. Rinnooy Kan, and L. Stougie.
Stochastic integer programming by dynamic programming.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 403-412. Springer, Berlin, 1988.
Constantino M. Lagoa, Xiang Li, and Mario Sznaier.
Probabilistically constrained linear programs and risk-adjusted
controller design.
SIAM J. Optim., 15(3):938-951 (electronic), 2005.
Hang-Chin Lai and Kensuke Tanaka.
On continuous-time discounted stochastic dynamic programming.
Appl. Math. Optim., 23(2):155-169, 1991.
T.L. Lai.
Stochastic approximation and sequential search for optimum.
In Proceedings of the Berkeley conference in honor of Jerzy
Neyman and Jack Kiefer, Vol. II (Berkeley, Calif., 1983), Wadsworth
Statist./Probab. Ser., pages 557-577, Belmont, Calif., 1985. Wadsworth.
Kim Fung Lam and Jane W. Moy.
A simple weighting scheme for classification in two-group
discriminant problems.
Comput. Oper. Res., 30(1):155-164, 2003.
K. Lampe.
Numerischer Vergleich des dualen und primalen
Vektoroptimierungsproblemms mit dem stochastischen Suchverfahren von Timmel.
Wiss. Z. Tech. Hochsch. Ilmenau 31, No.2, 93-100, 1985.
M.N. Lane and S.C. Littlechild.
A stochastic programming approach to weather-dependent pricing for
water resources.
In Stochastic programming, Proc. int. Conf., Oxford 1974,
357-368, 1980.
G. Laporte, F.V. Louveaux, and L. van Hamme.
Exact solution of a stochastic location problem by an integer
L-shaped algorithm.
Transportation Science, 28(2):95-103, 1994.
Gilbert Laporte and Francois Louveaux.
Formulations and bounds for the stochastic capacitated vehicle
routing problem with uncertain supplies.
In Economic decision-making: games, econometrics and
optimisation, Contrib. in Honour of J. H. Dreze, 443-455, 1990.
Gilbert Laporte, Francois Louveaux, and Helene Mercure.
The vehicle routing problem with stochastic travel times.
Transp. Sci. 26, No.3, 161-170, 1992.
Gilbert Laporte and François V. Louveaux.
The integer L-shaped method for stochastic integer programs with
complete recourse.
Oper. Res. Lett., 13(3):133-142, 1993.
Gilbert Laporte, François V. Louveaux, and Hélène Mercure.
A priori optimization of the probabilistic traveling salesman
problem.
Oper. Res., 42(3):543-549, 1994.
Yu. P. Laptin.
Construction of heuristic search strategies in the branch-and-bound
method in solving the multidimensional knapsack problem.
In Methods of investigation of extremal problems, pages
109-115, 124, Kiev, 1981. Akad. Nauk Ukrain. SSR Inst. Kibernet.
Robert E. Larson and John L. Casti.
Principles of dynamic programming. Part II: Advanced theory and
applications.
Control and Systems Theory, Vol. 7. New York and Basel: Marcel
Dekker, Inc., 1982.
J.B. Lasserre.
Exact formula for sensitivity analysis of Markov chains.
J. Optim. Theory Appl., 71(2):407-413, 1991.
J.B. Lasserre, C. Bes, and F. Roubellat.
The stochastic discrete dynamic lot size problem: an open-loop
solution.
Operations Research, 33(3):684-689, 1985.
Jean B. Lasserre.
Duality and randomization in nonlinear programming.
ANZIAM J., 42((E)):E27-E68, 2000/01.
Karen K. Lau and Robert S. Womersley.
Multistage quadratic stochastic programming.
J. Comput. Appl. Math., 129(1-2):105-138, 2001.
Nonlinear programming and variational inequalities (Kowloon, 1998).
T. W. Edward Lau and Y. C. Ho.
Universal alignment probabilities and subset selection for ordinal
optimization.
J. Optim. Theory Appl., 93(3):455-489, 1997.
Purushottam W. Laud, L. Mark Berliner, and Prem K. Goel.
A stochastic probing algorithm for global optimization.
J. Global Optim., 2(2):209-224, 1992.
Jean-Louis Lauriere.
Elements de programmation dynamique. Preface de Robert Faure.
Recherche Operationnelle Appliquee, 3. Collection "Programmation".
Paris: Gauthier-Villars., 1979.
Irving H. LaValle.
On information-augmented chance-constrained programs.
Oper. Res. Lett., 4(5):225-230, 1986.
Irving H. Lavalle.
On the "Bayesability" of chance-constrained programming problems.
Oper. Res. Lett., 4(6):281-283, 1986.
Irving H. Lavalle.
On the Bayesability of chance-constrained programs: a
clarification.
Oper. Res. Lett., 5(5):261, 1986.
Irving H. Lavalle.
Response to: "Use of sample information in stochastic recourse and
chance-constrained programming models" [Management Sci. 31
(1985), no. 1, 96-108; MR 87e:90074] by R. Jagannathan: on the
"Bayesability" of CCPs.
Management Sci., 33(10):1224-1231, 1987.
With a reply by Jagannathan.
¯E. P. Lavrinenko.
A certain stochastic programming problem.
In Cybernetics and computer technology, No. 8: Complex control
systems (Russian), pages 68-71, Kiev, 1971. "Naukova Dumka".
¯E. P. Lavrinenko.
Pareto-optimal solutions in problems of vector stochastic
optimization.
In Questions in the study of multicriteria optimization
problems, volume 22 of Preprint 81, pages 40-51, 55. Akad. Nauk
Ukrain. SSR Inst. Kibernet., Kiev, 1981.
E.P. Lavrinenko.
Ueber ein Problem der Programmsteuerung im hierarchischen
Zweiniveausystem unter Unbestimmtheitsverhaeltnissen.
In Kibernetika vycislit. Tehn. 19, slozn. Sist. Upravl.,
9-13, 1973.
J.P. Lawrence III and Kenneth Steiglitz.
Randomized pattern search.
IEEE Trans. Comput. C- 21, 382-385, 1972.
Andrew J. Lazarus.
Certain expected values in the random assignment problem.
Oper. Res. Lett., 14(4):207-214, 1993.
Jean-Sébastien Le Brizaut.
Une méthode d'optimisation stochastique pour évaluer des minima
à e près.
Bull. Sci. Math., 126(8):693-703, 2002.
Larry J. LeBlanc.
Optimization models for distribution planning.
In Risk and capital (Ulm, 1983), volume 227 of Lecture
Notes in Econom. and Math. Systems, pages 203-223. Springer, Berlin, 1984.
J.P. Leclercq.
La programmation linéaire stochastique: une approche
multicritère. I. Formulation.
Cahiers Centre Études Rech. Opér., 23(1):31-41, 1981.
J.P. Leclercq.
La programmation linéaire stochastique: une approche
multicritère. II. Un algorithme interactif adapté aux distributions
multinormales.
Cahiers Centre Études Rech. Opér., 23(2):121-132, 1981.
J.P. Leclercq.
Stochastic programming: an interactive multicriteria approach.
European J. Oper. Res., 10(1):33-41, 1982.
Pierre L'Ecuyer and Alain Haurie.
Preventive replacement for multicomponent systems: An opportunistic
discrete-time dynamic programming model.
IEEE Trans. Reliab. R-32, 117-118, 1983.
Pierre L'Ecuyer and Gaétan Perron.
On the convergence rates of IPA and FDC derivative
estimators.
Oper. Res., 42(4):643-656, 1994.
Sang M. Lee and David L. Olson.
A gradient algorithm for chance constrained nonlinear goal
programming.
Eur. J. Oper. Res. 22, 359-369, 1985.
Peter P. Lehleiter.
Optimale Steuerung der Bedienung exponentieller Wartesysteme mit
vorgegebener Kundenzahl. (Optimal service control for exponential queueing
systems with predetermined number of customers)., 1984.
Guiyuan Lei.
Adaptive quasi-Monte Carlo method for multiple-extrema
optimization.
Control Theory Appl., 19(3):431-434, 2002.
Zhongxue Lei and Xianyu Li.
Several convexity statements in chance constrained programming.
J. Jiangxi Norm. Univ., Nat. Sci. Ed. 13, No.2, 13-19, 1989.
Timo Leipälä.
On the solutions of stochastic traveling salesman problems.
European J. Oper. Res., 2(4):291-297, 1978.
Bernard Lemaire.
About the convergence of the proximal method.
In Advances in optimization (Lambrecht, 1991), volume 382 of
Lecture Notes in Econom. and Math. Systems, pages 39-51. Springer,
Berlin, 1992.
B.J. Lence and A. Ruszczynski.
Managing water quality under uncertainty: Application of a new
stochastic branch and bound method.
IIASA Working Paper WP-96-066, Laxenburg, Austria, 1996.
J.K. Lenstra.
Corrigendum: "Stochastic integer programming by dynamic
programming" [Statist. Neerlandica 39 (1985), no. 2, 97-113;
MR 86j:90154] by B. J. Lageweg, Lenstra, A. H. G. Rinnooy
Kan and L. Stougie.
Statist. Neerlandica, 40(2):129, 1986.
J.K. Lenstra, A.H.G. Rinnooy Kan, and L. Stougie.
A framework for the probabilistic analysis of hierarchical planning
systems.
In Stochastics and optimization, Sel. Pap. ISSO Meet.,
Gorguano/Italy 1982, Ann. Oper. Res. 1, 23-42, 1984.
Cornelius T. Leondes and Ranjit K. Nandi.
Capacity expansion in convex cost networks with uncertain demand.
Operations Res., 23(6):1172-1178, 1975.
S.L. Leonov.
A stochastic algorithm for the minimization of an additive function.
Avtomat. i Telemekh., 10:63-70, 1985.
Cristina M.A. Leopoldino, Mário V.F. Pereira, Leontina M.V. Pinto, and
Celso C. Ribeiro.
A constraint generation scheme to probabilistic linear problems with
an application to power system expansion planning.
Ann. Oper. Res., 50:367-385, 1994.
Applications of combinatorial optimization.
R. Lepp.
A projective stochastic approximation method for finding an
admissible point in stochastic programming.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat.,
26(2):128-134, 1977.
R. Lepp.
Deterministic equivalents of stochastic programming problems with
elliptically symmetric distributions.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat.,
28(2):158-160, 180, 1979.
R. Lepp.
Maximization of a probability function on simple sets.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat.,
28(4):303-309, 1979.
R. Lepp.
Minimization of a smooth function under probabilistic constraints.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat.,
29(2):140-144, 229, 1980.
R. Lepp.
Stochastic approximation type algorithm for the maximization of the
probability function.
Izv. Akad. Nauk Ehst. SSR, Fiz., Mat. 32, 150-156, 1983.
R. Lepp.
Conditions for discrete stability of the general stochastic
programming problem with decision functions.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat., 35(1):20-27,
122, 1986.
R. Lepp.
Discrete stability of stochastic programming problems with
recourse.
In System modelling and optimization, Proc. 12th IFIP Conf.,
Budapest/Hung. 1985, Lect. Notes Control Inf. Sci. 84, 529-534, 1986.
R. Lepp.
On the approximation of stochastic convex programming problems.
In Stochastic optimization (Kiev, 1984), volume 81 of
Lecture Notes in Control and Inform. Sci., pages 427-434. Springer, Berlin,
1986.
R. Lepp.
Discrete approximation of linear two-stage stochastic programming
problem.
Numer. Funct. Anal. Optim., 9(1-2):19-33, 1987.
R. Lepp and V. Ol'man.
An inequality for integrals with spherically symmetric functions and
its application to optimization.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat.,
29(2):133-139, 229, 1980.
R. Lepp and E. Raik.
Randomized solutions in stochastic programming problems.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat., 21:379-386,
1972.
R. È. Lepp.
Approximation of a problem of operative stochastic programming.
Issled. Operatsii i ASU, 26:20-26, 117, 1985.
R. È. Lepp.
Approximation of the probability functional.
In Analysis of stochastic systems by methods in operations
research and reliability theory (Russian), pages 17-21, ii, Kiev, 1987.
Akad. Nauk Ukrain. SSR Inst. Kibernet.
R. È. Lepp.
Approximation of a problem of stochastic programming with complete
recursion.
Dokl. Akad. Nauk SSSR, 305(6):1307-1310, 1989.
R.E. Lepp.
Investigations of Estonian scientists on stochastic programming.
Izv. Akad. Nauk SSSR, Tekh. Kibern. 1982, No.6, 57-64, 1982.
R.E. Lepp.
Investigations of Estonian scientists on stochastic programming.
Engrg. Cybernetics, 20(6):11 (1983), 1982.
R.È. Lepp.
Discrete approximation and stability of a stochastic programming
problem with complete recursion.
Zh. Vychisl. Mat. i Mat. Fiz., 27(7):993-1004, 1117, 1987.
R.E. Lepp.
Approximation of decision rules in stochastic programming.
J. Comput. Systems Sci. Internat., 31(2):46-49, 1993.
Riho Lepp.
Approximate solution of stochastic programming problems with
recourse.
Kybernetika (Prague), 23(6):476-482, 1987.
Riho Lepp.
Approximations to stochastic programs with complete recourse.
SIAM J. Control Optim., 28(2):382-394, 1990.
Riho Lepp.
Discrete approximation of extremum problems with operator
constraints.
In System modelling and optimization (Leipzig, 1989), volume
143 of Lecture Notes in Control and Inform. Sci., pages 170-176.
Springer, Berlin, 1990.
Riho Lepp.
Projection and discretization methods in stochastic programming.
J. Comput. Appl. Math., 56(1-2):55-64, 1994.
Stochastic programming: stability, numerical methods and applications
(Gosen, 1992).
Riho Lepp.
Approximation of extremum problems with probability cost functionals.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 169-185. Springer, Berlin, 1998.
Riho Lepp.
Approximation of the quantile minimization problem with decision
rules.
Optim. Methods Softw., 17(3):505-522, 2002.
Stochastic programming.
Riho Lepp.
Discrete approximation of extremum problems with chance constraints.
In Stochastic optimization techniques (Neubiberg/Munich, 2000),
volume 513 of Lecture Notes in Econom. and Math. Systems, pages 21-33.
Springer, Berlin, 2002.
L.L. Leshchenko.
On a stochastic transportation problem.
Issled. Oper. ASU 23, 43-50, 1984.
Reuven R. Levary.
An experimental sequential solution procedure to stochastic linear
programming problems with 0-1 variables.
Internat. J. Systems Sci., 15(10):1073-1085, 1984.
V.L. Levin.
Extremal problems with probability measures, functionally closed
preorders and strong stochastic dominance.
In Stochastic optimization, Proc. Int. Conf., Kiev/USSR 1984,
Lect. Notes Control Inf. Sci. 81, 435-447, 1986.
Art Lew.
Nondeterministic dynamic programming on a parallel coprocessing
system.
Appl. Math. Comput., 120(1-3):139-147, 2001.
The Bellman continuum (Santa Fe, NM, 1999).
D. Li, X. L. Sun, M. P. Biswal, and F. Gao.
Convexification, concavification and monotonization in global
optimization.
Ann. Oper. Res., 105:213-226 (2002), 2001.
Geometric programming.
Donghui Li.
Two methods for undiscounted Markov decision programming.
Hunan Ann. Math. 7, No.2, 97-103, 1987.
Duan Li.
Multiple objectives and nonseparability in stochastic dynamic
programming.
Internat. J. Systems Sci., 21(5):933-950, 1990.
Duan Li and Yacov Y. Haimes.
The uncertainty sensitivity index method (USIM) and its extension.
Nav. Res. Logist. 35, No.6, 655-672, 1988.
Hua Li and Xing Si Li.
Solutions of minimum cross-entropy optimization problems with
cross-entropy constraints.
J. Lanzhou Univ. Technol., 32(1):148-151, 2006.
Wu-Ji Li and J. MacGregor Smith.
Stochastic quadratic assignment problems.
In Quadratic assignment and related problems (New Brunswick, NJ,
1993), volume 16 of DIMACS Ser. Discrete Math. Theoret. Comput. Sci.,
pages 221-236. Amer. Math. Soc., Providence, RI, 1994.
Xiao Li Li and Gong Yan Lei.
Some comments about stochastic optimization algorithms.
Math. Numer. Sinica, 18(4):435-441, 1996.
Xiao Li Li and Jian Hui Ren.
Stability of optimal values and optimal solution sets of stochastic
programming.
J. Shaanxi Normal Univ. Nat. Sci. Ed., 32(4):27-30, 2004.
Xiao Liu Li and Jin De Wang.
Approximate feasible direction method for stochastic programming
problems with recourse. Linear inequality deterministic constraints.
Optimization, 21(3):401-407, 1990.
X.S. Li and A.B. Templeman.
An informational entropy approach to optimization.
In Approximation, optimization and computing, pages 263-266.
North-Holland, Amsterdam, 1990.
Y. Li and D. Wang.
A semi-infinite programming model for earliness/tardiness production
planning with simulated annealing.
Math. Comput. Modelling, 26(7):35-42, 1997.
Y. P. Li, G. H. Huang, S. L. Nie, X. H. Nie, and I. Maqsood.
An interval-parameter two-stage stochastic integer programming model
for environmental systems planning under uncertainty.
Eng. Optim., 38(4):461-483, 2006.
Marek Libura.
Integer programming problems with inexact objective function.
Control Cybern. 9, 189-202, 1980.
Bennet P. Lientz.
Stochastic allocation of spare components.
In Optimizing Meth. Statist., Proc. Sympos. Ohio State Univ.
1971, 403-412 , 1971.
V.E. Lihtenstein.
Diskretheit und Zufaelligkeit in oekonomisch-mathematischen
Problemen. (Diskretnost' i slucainost' v ekonomiko-matematiceskih
zadacah.).
Moskau: Verlag 'Nauka'., 1973.
Gui-Hua Lin and Masao Fukushima.
A class of stochastic mathematical programs with complementarity
constraints: reformulations and algorithms.
J. Ind. Manag. Optim., 1(1):99-122, 2005.
Gui-Hua Lin and Masao Fukushima.
Regularization method for stochastic mathematical programs with
complementarity constraints.
ESAIM Control Optim. Calc. Var., 11(2):252-265 (electronic),
2005.
Gui-Hua Lin and Masao Fukushima.
New reformulations for stochastic nonlinear complementarity problems.
Optim. Methods Softw., 21(4):551-564, 2006.
Jianhua Lin and S.K.M. Wong.
Approximation of discrete probability distributions based on a new
divergence measure.
Congr. Numer., 61:75-80, 1988.
Seventeenth Manitoba Conference on Numerical Mathematics and
Computing (Winnipeg, MB, 1987).
S. Y. Lin and Y. C. Ho.
Universal alignment probability revisited.
J. Optim. Theory Appl., 113(2):399-407, 2002.
Xiaocang Lin and Loo Hay Lee.
A new approach to discrete stochastic optimization problems.
European J. Oper. Res., 172(3):761-782, 2006.
Zhenghua Lin and Weisheng Yu.
The twice continuously differentiable property of two-stage
stochastic linear programming problems with fixed recourse.
Soochow J. Math., 23(2):157-164, 1997.
Jeff Linderoth, Alexander Shapiro, and Stephen Wright.
The empirical behavior of sampling methods for stochastic
programming.
Ann. Oper. Res., 142:215-241, 2006.
Jeff Linderoth and Stephen Wright.
Decomposition algorithms for stochastic programming on a
computational grid.
Stochastic Programming E-Print Series, http://www.speps.org,
2001.
Jeff Linderoth and Stephen Wright.
Decomposition algorithms for stochastic programming on a
computational grid.
Optimization Online, http://www.optimization-online.org, 2001.
Jeff Linderoth and Stephen J. Wright.
Computational grids for stochastic programming.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 61-77. SIAM, Philadelphia, PA, 2005.
Chen Ling, Xiaojun Chen, Masao Fukushima, and Liqun Qi.
A smoothing implicit programming approach for solving a class of
stochastic generalized semi-infinite programming problems.
Pac. J. Optim., 1(1):127-145, 2005.
B.P. Lingaraj.
Certainty equivalent of a chance constraint if the random variable is
uniformly distributed.
Comm. Statist., 3(10):949-951, 1974.
B.P. Lingaraj and Harvey Wolfe.
Certainty equivalent of a chance constraint if the random variable
follows a gamma distribution.
Sankhy¯a Ser. B, 36(2):204-208, 1974.
K. Linowsky and A. B. Philpott.
On the convergence of sampling-based decomposition algorithms for
multistage stochastic programs.
J. Optim. Theory Appl., 125(2):349-366, 2005.
Svante Linusson and Johan Wästlund.
A proof of Parisi's conjecture on the random assignment problem.
Probab. Theory Related Fields, 128(3):419-440, 2004.
Joseph Lipscomb and David Zalkind.
Deterministic models for production of services with stochastic
technology.
Econometrica, 48(5):1169-1186, 1980.
Baoding Liu.
Dependent-chance goal programming and its genetic algorithm based
approach.
Math. Comput. Modelling, 24(7):43-52, 1996.
Baoding Liu.
Dependent-chance programming: a class of stochastic optimization.
Comput. Math. Appl., 34(12):89-104, 1997.
Chih-Ming Liu and Jerry L. Sanders.
Stochastic design optimization of asynchronous flexible assembly
systems.
Ann. Oper. Res. 15, 131-154, 1988.
Chuan Cai Liu.
Convergence rate of moments for stochastic perturbation gradient
approximation.
J. Fuzhou Univ. Nat. Sci. Ed., 30(1):28-32, 2002.
M.L. Liu and N.V. Sahinidis.
Optimization in process planning under uncertainty.
Industrial & Engineering Chemistry Research, 35(11):4154-4165,
1996.
P.T. Liu and J.G. Sutinen.
Pareto optimal leasing and investment policies for a publicly owned
exhaustible resource.
In New trends in dynamic system theory and economics, Proc.
Semin., Udine/Italy 1977, 137-149, 1979.
W. Liu and Y. H. Dai.
Minimization algorithms based on supervisor and searcher cooperation.
J. Optim. Theory Appl., 111(2):359-379, 2001.
X. W. Liu and M. Fukushima.
Parallelizable preprocessing method for multistage stochastic
programming problems.
J. Optim. Theory Appl., 131(3):327-346, 2006.
Xian Liu.
Several filled functions with mitigators.
Appl. Math. Comput., 133(2-3):375-387, 2002.
Xian Liu.
The role of stochastic programming in communication network design.
Comput. Oper. Res., 32(9):2329-2349, 2005.
Xinwei Liu and Jie Sun.
A new decomposition technique in solving multistage stochastic linear
programs by infeasible interior point methods.
J. Global Optim., 28(2):197-215, 2004.
Yian-Kui Liu and Baoding Liu.
Random fuzzy programming with chance measures defined by fuzzy
integrals.
Math. Comput. Modelling, 36(4-5):509-524, 2002.
John R. Liukkonen and Arnold Levine.
On convergence of iterated random maps.
SIAM J. Control Optim., 32(6):1752-1762, 1994.
Liwan Liyanage and J. George Shanthikumar.
Allocation through stochastic Schur convexity and stochastic
transposition increasingness.
In Stochastic inequalities (Seattle, WA, 1991), volume 22 of
IMS Lecture Notes Monograph Ser., pages 253-273. Inst. Math. Statist.,
Hayward, CA, 1992.
S.I. Ljasko and F. Mirzoahmedov.
Method of feasible directions in the minimization of almost
differentiable functions with unknown parameters.
Akad. Nauk Ukrain. SSR Inst. Kibernet. Preprint, 13:14-20, 53,
1979.
Stochastic optimization models.
L. Ljung.
Some basic ideas in recursive identification.
In Analysis and optimisation of stochastic systems, Proc. int.
Conf., Oxford 1978, 408-418, 1980.
Lennart Ljung.
Recursive identification.
In Stochastic systems: the mathematics of filtering and
identification and applications, Proc. NATO Adv. Study Inst., Les
Arcs/Savoie/ France 1980, 247-281, 1981.
C. Lobry.
Une propriete de l'ensemble des etats accessibles d'un systeme
guidable.
C. R. Acad. Sci., Paris, Ser. A 272, 153-156, 1971.
M. Locatelli.
Bayesian algorithms for one-dimensional global optimization.
J. Global Optim., 10(1):57-76, 1997.
M. Locatelli and F. Schoen.
Simple linkage: analysis of a threshold-accepting global optimization
method.
J. Global Optim., 9(1):95-111, 1996.
Marco Locatelli and Fabio Schoen.
An adaptive stochastic global optimization algorithm for
one-dimensional functions.
Ann. Oper. Res., 58:263-278, 1995.
Applied mathematical programming and modeling, II (APMOD 93)
(Budapest, 1993).
Marco Locatelli and Fabio Schoen.
Theoretical and experimental analysis of random linkage algorithms
for global optimization.
In System modelling and optimization (Prague, 1995), pages
473-480. Chapman & Hall, London, 1996.
Marco Locatelli and Fabio Schoen.
Random Linkage: a family of acceptance/rejection algorithms for
global optimisation.
Math. Program., 85(2, Ser. A):379-396, 1999.
A.G. Lockett and A.P. Muhlemann.
A stochastic programming model for aggregate production planning.
Eur. J. Oper. Res. 2, 350-356, 1978.
A.G. Lockett, A.P. Muhlemann, and L.A. Wolsey.
A stochastic programming model for project selection.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 427-448. Academic Press, London, 1980.
A. Løkketangen and D.L. Woodruff.
Progressive hedging and tabu search applied to mixed integer (0,1)
multi-stage stochastic programming.
Journal of Heuristics, 2:111-128, 1996.
K. Lommatzsch (ed.).
Programmpakete der Operationsforschung.
Seminarber., Humboldt-Univ. Berlin, Sekt. Math. 22, 160 P.,
1979.
Domingo López-Rodríguez, Enrique Mérida-Casermeiro, and J. M.
Ortiz-de Lazcano-Lobato.
Stochastic multivalued network for optimization: application to the
graph maxcut problem.
WSEAS Trans. Math., 6(3):500-505, 2007.
Victor Loskutov.
Models, algorithms and software of stochastic optimization.
In Computational aspects of model choice (Prague, 1991),
Contrib. Statist., pages 177-186. Physica, Heidelberg, 1993.
D. P. Loucks.
Computer models for reservoir regulation.
Journal of the Sanitary Engineering Division, Proceedings of the
American Society of Civil Engineers, 94(SA4):657-669, August 1968.
Daniel P. Loucks.
Discrete chance constrained models for river basin planning.
In Stochastic programming, Proc. int. Conf., Oxford 1974,
329-340, 1980.
F. Louveaux.
Optimal scheduling of income tax prepayments under stochastic
incomes.
Eur. J. Oper. Res. 9, 26-32, 1982.
Francois Louveaux and Jacques-Francois Thisse.
Production and location on a network under demand uncertainty.
Oper. Res. Lett. 4, 145-149, 1985.
François V. Louveaux.
A solution method for multistage stochastic programs with recourse
with application to an energy investment problem.
Oper. Res., 28(4):889-902, 1980.
François V. Louveaux.
Multistage stochastic programs with block-separable recourse.
Math. Programming Stud., 28:48-62, 1986.
Stochastic programming 84. II.
François V. Louveaux and D. Peeters.
A dual-based procedure for stochastic facility location.
Oper. Res., 40(3):564-573, 1992.
François V. Louveaux and Maarten H. van der Vlerk.
Stochastic programming with simple integer recourse.
Math. Programming, 61(3, Ser. A):301-325, 1993.
F.V. Louveaux.
Stochastic programs with simple integer recourse.
Manuscript, Facultés Universitaires Notre-Dame de la Paix, Namur,
1991.
F.V. Louveaux and Y. Smeers.
Optimal investments for electricity generation: A stochastic model
and a test-problem.
In Numerical techniques for stochastic optimization, Springer
Ser. Comput. Math. 10, 445-453, 1988.
William S. Lovejoy.
Policy bounds for Markov decision processes.
Oper. Res. 34, 630-637, 1986.
David Lowell Lovelady.
Iterative maximization subject to stochastic constraints.
J. Math. Anal. Appl. 64, 142-145, 1978.
Dinh The' Lu'c.
Duality in programming under probabilistic constraints with a random
technology matrix.
Probl. Control Inf. Theory 12, 429-437, 1983.
R. Lucchetti and R.J-B. Wets.
Convergence of minima of integral functionals with applications to
optimal control and stochastic optimization.
Statistics and Decisions, 11, 1993.
James Luedtke and Shabbir Ahmed.
A sample approximation approach for optimization with probabilistic
constraints.
Optimization Online, http://www.optimization-online.org, 2007.
James Luedtke, Shabbir Ahmed, and George Nemhauser.
An integer programming approach for linear programs with
probabilistic constraints.
Optimization Online, http://www.optimization-online.org, 2007.
M.K. Luhandjula.
Flou et aleatoire en programmation lineaire.
BUSEFAL 11, 33-38, 1982.
M.K. Luhandjula.
Programmation lineaire avec des donnees possibilistes.
BUSEFAL 15, 134-143, 1983.
M.K. Luhandjula.
Satisfying solutions for a possibilistic linear program.
Inf. Sci. 40, 247-265, 1986.
M.K. Luhandjula.
Linear programming with a possibilistic objective function.
Eur. J. Oper. Res. 31, 110-117, 1987.
M.K. Luhandjula and M.M. Gupta.
On fuzzy stochastic optimization.
Fuzzy Sets and Systems, 81(1):47-55, 1996.
Fuzzy optimization.
Monga Kalonda Luhandjula.
Linear programming under randomness and fuzziness.
Fuzzy Sets Syst. 10, 45-55, 1983.
S. Lukasik.
Optimale Steuerung in hierarchischen mehrkriteriellen Systemen
unter den Bedingungen unvollständiger Information.
In Polyoptimization-theoretic foundations and applications
(Sem., Bad Stuer, 1976) (German), volume 2 of ZKI-Inform. 78, pages
87-103. Akad. Wiss. DDR, Berlin, 1978.
Guglielmo Lulli and Suvrajeet Sen.
A branch-and-price algorithm for multi-stage stochastic integer
programming with application to stochastic batch-sizing problems.
Stochastic Programming E-Print Series, http://www.speps.org,
2002.
Guglielmo Lulli and Suvrajeet Sen.
A heuristic procedure for stochastic integer programs with complete
recourse.
European J. Oper. Res., 171(3):879-890, 2006.
R.R. Luman.
Quantitative decision support for upgrading complex systems of
systems.
Ph.D. thesis, School of Engineering and Applied Science, George
Washington University, 1997.
R.R. Luman.
Upgrading complex systems of systems: a CAIV methodology for
warfare area requirements analysis.
Military Operations Research, 52:53-75, 2000.
Morten W. Lund.
Valuing flexibility in offshore petroleum projects.
Ann. Oper. Res., 99:325-349 (2001), 2000.
Applied mathematical programming and modeling, IV (Limassol, 1998).
M. Lundy and A. Mees.
Convergence of an annealing algorithm.
Math. Programming, 34(1):111-124, 1986.
Chengjie Luo, Clement Yu, Jorge Lobo, Gaoming Wang, and Tracy Pham.
Computation of best bounds of probabilities from uncertain data.
Comput. Intelligence, 12(4):541-566, 1996.
Jian Wen Luo and Shi Jie Lu.
Convergence of approximate solutions in stochastic programming.
J. Zhejiang Univ. Sci. Ed., 27(5):493-497, 2000.
Jian Wen Luo and Shi Jie Lu.
Stability analysis for probabilistic constrained programs.
Appl. Math. J. Chinese Univ. Ser. A, 16(1):119-124, 2001.
Jian Wen Luo and Jin De Wang.
Weak differentiability in stochastic programming.
Gaoxiao Yingyong Shuxue Xuebao Ser. A, 12(1):53-62, 1997.
Irvin J. Lustig, John M. Mulvey, and Tamra J. Carpenter.
Formulating two-stage stochastic programs for interior point methods.
Oper. Res., 39(5):757-770, 1991.
S.I. Lyashko and F. Mirzoakhmedov.
Some methods of joint identification and optimization.
Dokl. Akad. Nauk Tadzh. SSR 24, 721-727, 1981.
S.I. Lyashko and F. Mirzoakhmedov.
Some methods of simultaneous identification and optimization.
Dokl. Akad. Nauk Tadzhik. SSR, 24(12):721-724, 1981.
Norman R. Lyons.
Resource planning for randomly arriving stochastic jobs.
Management Sci., Appl. 21, 931-936, 1975.
G. I. Lyubchenko and A. N. Nakonechnyi.
Optimization methods for compound Poisson risk processes.
Kibernet. Sistem. Anal., (2):87-96, 188, 1998.
A. Maatman, C. Schweigman, A. Ruijs, and M.H. van der Vlerk.
Modeling farmers' response to uncertain rainfall in burkina faso: a
stochastic programming approach.
Operations Research, 50(3):399-414, 2002.
Odile Macchi and Eweda Eweda.
Second-order convergence analysis of stochastic adaptive linear
filtering.
IEEE Trans. Autom. Control AC-28, 76-85, 1983.
M. E. P. Maceira and J. M. Damázio.
Use of the PAR(p) model in the stochastic dual dynamic
programming optimization scheme used in the operation planning of the
Brazilian hydropower system.
Probab. Engrg. Inform. Sci., 20(1):143-156, 2006.
R.Infante Macias.
Bemerkung ueber lineare stochastische Programmierung: Entwicklung
und aktueller Stand. I.
Trabajos Estadist. Investig. operat. 23, No.1/2, 9-49, 1972.
Leonard MacLean, Rafael Sanegre, Yonggan Zhao, and William T. Ziemba.
Capital growth with security.
Stochastic Programming E-Print Series, http://www.speps.org,
2002.
Leonard C. MacLean and William T. Ziemba.
Growth versus security tradeoffs in dynamic investment analysis.
Ann. Oper. Res., 85:193-225, 1999.
Stochastic programming. State of the art, 1998 (Vancouver, BC).
Leonard C. MacLean, William T. Ziemba, and Yuming Li.
Time to wealth goals in capital accumulation.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Marilyn J. Maddox and John R. Birge.
Using second moment information in stochastic scheduling.
In Recent advances in control and optimization of manufacturing
systems, volume 214 of Lecture Notes in Control and Inform. Sci.,
pages 99-120. Springer, London, 1996.
G. Mádi-Nagy.
A method to find the best bounds in a multivariate discrete moment
problem if the basis structure is given.
Studia Sci. Math. Hungar., 42(2):207-226, 2005.
Y. Maeda.
Time difference simultaneous perturbation method.
Electronics Letters, 32:1016-1018, 1996.
Y. Maeda and R.J.P. De Figueiredo.
Learning rules for neuro-controller via simultaneous perturbation.
IEEE Transactions on Neural Networks, 8:1119-1130, 1997.
Y. Maeda, H. Hirano, and Y. Kanata.
A learning rule of neural networks via simultaneous perturbation and
its hardware implementation.
Neural Networks, 8:251-259, 1995.
Y. Maeda, A. Nakazawa, and K. Yakichi.
Hardware implementation of a pulse density neural network using
simultaneous perturbation learning rule.
Analog Intergrated Circuits and Signal Processing, 18:153-162,
1999.
P.M. Maekilae.
A self-tuning regulator based on optimal output feedback theory.
Automatica 20, 671-679, 1984.
F. Maffioli, M.G. Speranza, and C. Vercellis.
Randomized algorithms: An annotated bibliography.
In Stochastics and optimization, Sel. Pap. ISSO Meet.,
Gorguano/Italy 1982, Ann. Oper. Res. 1, 331-345, 1984.
P. Maillard.
Contribution à l'étude des problèmes de transport.
Publ. Économétriques, 10(1):19-41, 87, 1977.
A.A.K. Majumdar.
On a generalized secretary problem with three stops under a linear
travel cost.
Indian J. Math., 33(2):189-202, 1991.
A.A.K. Majumdar.
A best-choice problem with three stops and random termination.
Sankhy¯a Ser. B, 57(1):76-84, 1995.
Mukul Majumdar.
A note on learning and optimal decisions with a partially observable
state space.
In Essays in the economics of renewable resources, Contrib.
econ. Anal. 143, 141-153, 1982.
Mukul Majumdar and Tapan Mitra.
Robust ergodic chaos in discounted dynamic optimization models.
Econom. Theory, 4(5):677-688, 1994.
Mukul Majumdar and Itzhak Zilcha.
Optimal growth in a stochastic environment: Some sensitivity and
turnpike results.
J. Econ. Theory 43, 116-133, 1987.
W.K. Mak, D.P. Morton, and R.K. Wood.
Monte carlo bounding techniques for determining solution quality in
stochastic programs.
Operations Research Letters, 24:47-56, 1999.
Algirdas Makauskas.
On a possibility to use gradients in statistical models of global
optimization of objective functions.
Informatica, 2(2):248-254, 316, 324, 1991.
Yu.I. Maksimov.
Stochastic simultion in planning. (Stokhasticheskoe
modelirovanie v planirovanii).
Akademiya Nauk SSSR, Sibirskoe Otdelenie. Institut Ehkonomiki i
Organizatsii Promyshlennogo Proizvodstva. Novosibirsk: Izdatel'stvo "Nauka",
Sibirskoe Otdelenie., 1981.
Scott A. Malcolm and Stavros A. Zenios.
Robust optimization for power systems capacity expansion under
uncertainty.
J. Oper. Res. Soc. 45, No.9, 1040-1049, 1994.
Wilfredo L. Maldonado and B. F. Svaiter.
Hölder continuity of the policy function approximation in the value
function approximation.
J. Math. Econom., 43(5):629-639, 2007.
Lilia Maliar and Serguei Maliar.
Solving nonlinear dynamic stochastic models: an algorithm computing
value function by simulations.
Econom. Lett., 87(1):135-140, 2005.
W. Maly and S.W. Director.
Dimension reduction procedure for the simplicial approximation
approach to design centering.
Proc. IEE-G, 127(6):255-259, 1980.
V.V. Malyshev, A.I. Kibzun, and D. È. Chernov.
Two approaches to the solution of probabilistic optimization
problems.
Soviet J. Automat. Inform. Sci., 20(3):20-25 (1988), 1987.
John W. Mamer and Kenneth E. Schilling.
On the growth of random knapsacks.
Discrete Appl. Math. 28, No.3, 223-230, 1990.
A. S. Manne and T. R. Olsen.
Greenhouse gas abatement-toward Pareto-optimal decisions under
uncertainty.
Annals of Operations Research, 68:267-279, 1996.
Alexander Mänz and Silvia Vogel.
On stability of multistage stochastic decision problems.
In Recent advances in optimization, volume 563 of Lecture
Notes in Econom. and Math. Systems, pages 103-118. Springer, Berlin, 2006.
Imran Maqsood, Guo H. Huang, and Julian Scott Yeomans.
An interval-parameter fuzzy two-stage stochastic program for water
resources management under uncertainty.
European J. Oper. Res., 167(1):208-225, 2005.
A. Marchetti Spaccamela, A.H.G. Rinnooy Kan, and L. Stougie.
Hierarchical vehicle routing problems.
Networks 14, 571-586, 1984.
A. Marchetti-Spaccamela and C. Vercellis.
Stochastic on-line knapsack problems.
Math. Programming, 68(1, Ser. A):73-104, 1995.
T.A. Marinchenko.
Application of stochastic quasigradient methods for dynamical
systems.
In Analysis of stochastic systems by methods in operations
research and reliability theory (Russian), pages 32-34, iii. Akad. Nauk
Ukrain. SSR Inst. Kibernet., Kiev, 1987.
Andreas Märkert and Rüdiger Schultz.
On deviation measures in stochastic integer programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Andreas Märkert and Rüdiger Schultz.
On deviation measures in stochastic integer programming.
Oper. Res. Lett., 33(5):441-449, 2005.
Giovanni Marro and Remo Rossi.
Condizioni di convessità nella programmazione dinamica.
Atti, Accad. Naz. Lincei Rend. Cl. Sci. Fis. Mat. Natur. (8),
54:604-614 (1974), 1973.
A. Martel and W. Price.
Stochastic programming applied to human resource planning.
J. Oper. Res. Soc. 32, 187-196, 1981.
Alain Martel.
Programme stochastique pour les plans hommes-machines a moyen
terme.
Revue Franc. Automat. Inform. Rech. operat. 8, V-1, 5-18, 1974.
Alain Martel.
A probabilistic assortment problem.
INFOR, Can. J. Operat. Res. Inform. Processing 15, 196-203,
1977.
Alain Martel and Ahmad Al-Nuaimi.
Tactical manpower planning via programming under uncertainty.
Operat. Res. Quart. 24, 571-585, 1973.
Alain Martel and Jean Ouellet.
Stochastic allocation of a resource among partially interchangeable
activities.
Eur. J. Oper. Res. 74, No.3, 528-539, 1994.
K. Marti.
Stochastische Programme in normierten Raeumen. (Stochastic
programming in normed spaces).
In Operations Research-Verfahren 1 130-132 (1971)., 1970.
K. Marti.
Konvexitaetsaussagen zum linearen stochastischen
Optimierungsproblem.
Z. Wahrscheinlichkeitstheorie Verw. Geb. 18, 159-166, 1971.
K. Marti.
Entscheidungsprobleme mit linearem Aktionen- und Ergebnisraum.
Z. Angew. Math. Mech., 53:T226-T228, 1973.
Vorträge der Wissenschaftlichen Jahrestagung der Gesellschaft für
Angewandte Mathematik und Mechanik (Ljubljana, 1972).
K. Marti.
Ueber ein Verfahren zur Loesung einer Klasse linearer
Entscheidungsprobleme.
Z. angew. Math. Mech. 54, Sonderheft, T 247 - T 248, 1974.
K. Marti.
Lösung stochastischer linearer Programme mit "complete
recourse" mittels stochastischer Penalty-Methoden.
Z. Angew. Math. Mech., 58(7):T491-T494, 1978.
K. Marti.
Diskretisierung stochastischer Programme unter Berücksichtigung
der Problemstruktur.
Z. Angew. Math. Mech., 59(3):T105-T108, 1979.
Vorträge der Wissenschaftlichen Jahrestagung der Gesellschaft für
Angewandte Mathematik und Mechanik, Teil I (Brussels, 1978).
K. Marti.
On approximative solutions of stochastic programming problems by
means of stochastic dominance and stochastic penalty methods.
In Survey of mathematical programming (Proc. Ninth Internat.
Math. Programming Sympos., Budapest, 1976), Vol. 2, pages 117-127,
Amsterdam, 1979. North-Holland.
K. Marti.
On accelerations of the convergence in random search methods.
Methods Oper. Res. 37, 391-406, 1980.
K. Marti.
On controlled random search procedures.
In Control applications of nonlinear programming and
optimization, Coll. Pap., 2nd IFAC Workshop, Oberpfaffenhofen/Ger. 1980,
214-223, 1980.
K. Marti.
Random search in optimization problems as a stochastic decision
process (adaptive random search).
Methods Oper. Res. 36, 223-234, 1980.
K. Marti.
Stochastic dominance and the construction of descent directions in
stochastic programs having a discrete distribution., 1980.
K. Marti.
Stochastische Dominanz und Konstruktion von Abstiegsrichtungen in
stochastischen Programmen bei Verteilungs-Symmetrien., 1980.
K. Marti.
On stochastic dominance and the construction of directions of
decrease in stochastic programs having a discrete distribution.
Methods Oper. Res. 41, 175-178, 1981.
K. Marti.
Ueber die Berechnung von Abstiegsrichtungen in Stochastischen
Linearen Programmen bei Verteilungsinvarianz.
Z. Angew. Math. Mech. 61, T341 - T343, 1981.
K. Marti.
Minimizing noisy objective functions by random search methods.
Z. Angew. Math. Mech. 62, T377 - T380, 1982.
K. Marti.
On the construction of descent directions in stochastic programs
having a discrete distribution.
Z. Angew. Math. Mech., 64(5):336-338, 1984.
K. Marti.
Computation of descent directions in stochastic optimization problems
with invariant distributions.
Z. Angew. Math. Mech., 65(8):355-378, 1985.
K. Marti.
Construction of descent directions in stochastic programs having a
discrete distribution. II.
Z. Angew. Math. Mech., 67(5):T408-T410, 1987.
K. Marti.
Descent stochastic quasigradient methods.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 393-401. Springer, Berlin, 1988.
K. Marti.
Optimal semi-stochastic approximation procedures. II.
Z. Angew. Math. Mech., 69(4):T67-T69, 1989.
K. Marti.
Optimal semistochastic approximation.
In Computing and computers for control systems (Paris, 1988),
volume 4 of IMACS Ann. Comput. Appl. Math., pages 409-415, Basel,
1989. Baltzer.
K. Marti.
Computation of efficient solutions of stochastic optimization
problems with applications to regression and scenario analysis.
In Stochastic versus fuzzy approaches to multiobjective
mathematical programming under uncertainty, volume 6 of Theory Decis.
Lib. Ser. D System Theory Knowledge Engrg. Probl. Solving, pages 163-188.
Kluwer Acad. Publ., Dordrecht, 1990.
K. Marti.
Stochastic optimization methods in structural mechanics.
Z. Angew. Math. Mech., 70(6):T742-T745, 1990.
Bericht über die Wissenschaftliche Jahrestagung der GAMM
(Karlsruhe, 1989).
K. Marti.
Stochastic programming: numerical solution techniques by
semi-stochastic approximation methods.
In Stochastic versus fuzzy approaches to multiobjective
mathematical programming under uncertainty, volume 6 of Theory Decis.
Lib. Ser. D System Theory Knowledge Engrg. Probl. Solving, pages 23-43.
Kluwer Acad. Publ., Dordrecht, 1990.
K. Marti.
Computation of efficient solutions of discretely distributed
stochastic optimization problems.
Z. Oper. Res., 36(3):259-294, 1992.
K. Marti.
Semi-stochastic approximation by the response surface methodology
(RSM).
Optimization, 25(2-3):209-230, 1992.
K. Marti.
Stochastic optimization in structural design.
Z. Angew. Math. Mech., 72(6):T452-T464, 1992.
Bericht über die Wissenschaftliche Jahrestagung der GAMM (Kraków,
1991).
K. Marti, editor.
Stochastic Optimization. Numerical Methods and Technical
Applications.
Springer, Berlin, 1992.
LN in Economics and Math. Systems 379.
K. Marti.
Satisficing techniques in stochastic linear programming.
Optimization, 31(4):359-384, 1994.
K. Marti.
Differentiation formulas for probability functions: the
transformation method.
Math. Programming, 75(2, Ser. B):201-220, 1996.
Approximation and computation in stochastic programming.
K. Marti.
Path planning for robots under stochastic uncertainty.
Optimization, 45(1-4):163-195, 1999.
Dedicated to the memory of Professor Karl-Heinz Elster.
K. Marti.
Stochastic programming methods in adaptive optimal trajectory
planning for robots.
ZAMM Z. Angew. Math. Mech., 82(11-12):795-809, 2002.
4th GAMM-Workshop "Stochastic Models and Control Theory"
(Lutherstadt Wittenberg, 2001).
K. Marti and E. Fuchs.
On the convergence rate of semistochastic approximation procedures.
Z. Angew. Math. Mech., 65(5):315-317, 1985.
K. Marti and E. Fuchs.
Computation of descent directions and efficient points in stochastic
optimization problems without using derivatives.
Math. Programming Stud., 28:132-156, 1986.
Stochastic programming 84. II.
K. Marti and E. Fuchs.
Rates of convergence of semistochastic approximation procedures for
solving stochastic optimization problems.
Optimization, 17(2):243-265, 1986.
K. Marti and E. Plöchinger.
Optimal semi-stochastic approximation procedures.
Z. Angew. Math. Mech., 68(5):T441-T443, 1988.
K. Marti and E. Plöchinger.
Optimal step sizes in semi-stochastic approximation procedures. I.
Optimization, 21(1):123-153, 1990.
K. Marti and E. Plöchinger.
Optimal step sizes in semistochastic approximation procedures.
II.
Optimization, 21(2):281-312, 1990.
K. Marti and R.-J. Riepl.
Optimale Portefeuilles mit stabil verteilten Renditen.
Z. Angew. Math. Mech., 57(5):T337-T339, 1977.
K. Marti and G. L. Tret¢yakov.
An algorithm for the approximate solution of the maximization problem
for a probability function on the basis of a star-shaped approximation.
Kurt Marti.
Konvexitätsaussagen zum linearen stochastischen
Optimierungsproblem.
Z. Wahrscheinlichkeitstheorie und Verw. Gebiete, 18:159-166,
1971.
Kurt Marti.
Entscheidungsprobleme mit linearem Aktionen- und Ergebnisraum.
Z. Wahrscheinlichkeitstheorie und Verw. Gebiete, 23:133-147,
1972.
Kurt Marti.
Approximationen der Entscheidungsprobleme mit linearer
Ergebnisfunktion und positiv homogener, subadditiver Verlustfunktion.
Z. Wahrscheinlichkeitstheorie und Verw. Gebiete, 31:203-233,
1974/75.
Kurt Marti.
Approximations to gradients in stochastic programming.
Bull. Inst. Internat. Statist., 46(4):137-140 (1976), 1975.
Kurt Marti.
Convex approximation of stochastic optimization problems.
In Operations Research Verfahren, Band XX, pages 66-76,
Meisenheim am Glan, 1975. Hain.
Kurt Marti.
Approximations to stochastic optimization problems.
In Optimization and operations research (Proc. Conf.,
Oberwolfach, 1975), pages 201-213. Lecture Notes in Econom. Math. Systems,
Vol. 117, Berlin, 1976. Springer.
Kurt Marti.
Stochastische Dominanz und stochastische lineare Programme.
In VIII. Oberwolfach-Tagung über Operations Research (1976),
Operations Res. Verfahren, XXIII, pages 141-160. Hain, Königstein/Ts.,
1977.
Kurt Marti.
On stochastic dominance relations in stochastic programming.
In Transactions of the Eighth Prague Conference on Information
Theory, Statistical Decision Functions, Random Processes (Prague, 1978), Vol.
B, pages 35-44. Reidel, Dordrecht, 1978.
Kurt Marti.
Stabilitaet approximativer Loesungen stochastischer Programme bei
Variationen der Parameterverteilung P.
In Oper. Res. Verf. 29, 2nd Symp. Oper. Res., Teil 2, Aachen
1977, 657-671 , 1978.
Kurt Marti.
Stochastic linear programs with random data having stable
distributions.
In Optimization techniques (Proc. 8th IFIP Conf., Würzburg,
1977), Part 2, pages 76-86. Lecture Notes in Control and Information Sci.,
Vol. 7, Berlin, 1978. Springer.
Kurt Marti.
Approximationen stochastischer Optimierungsprobleme,
volume 43 of Mathematical Systems in Economics.
Verlag Anton Hain, Königstein/Ts., 1979.
Kurt Marti.
On solutions of stochastic programming problems by descent procedures
with stochastic and deterministic directions.
In Third Symposium on Operations Research (Univ. Mannheim,
Mannheim, 1978), Section 3, volume 33 of Operations Res. Verfahren,
pages 281-293. Hain, Königstein/Ts., 1979.
Kurt Marti.
Approximations to stochastic optimization problems.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 159-166, London, 1980. Academic Press.
Kurt Marti.
On accelerations of the convergence in random search methods.
Mitteilung aus dem forschungsschwerpunkt simulation und optimierung
deterministischer und stochastischer dynamischer systeme, Hochschule der
Bundeswehr Muenchen, Neubibierg, 1980.
Kurt Marti.
Solving stochastic linear programs by semistochastic approximation
algorithms.
In Recent results in stochastic programming (Proc. Meeting,
Oberwolfach, 1979), volume 179 of Lecture Notes in Econom. and Math.
Systems, pages 191-213. Springer, Berlin, 1980.
Kurt Marti.
On accelerations of stochastic gradient methods by using more exact
gradient estimations.
In X symposium on operations research, Part I, Sections 1-5
(Munich, 1985), volume 53 of Methods Oper. Res., pages 327-336.
Athenäum/Hain/Hanstein, Königstein/Ts., 1986.
Kurt Marti.
Optimally controlled semi-stochastic approximation procedures.
In Ökonomie und Mathematik, pages 216-230, Berlin, 1987.
Springer.
Kurt Marti.
Descent directions and efficient solutions in discretely
distributed stochastic programs, volume 299 of Lecture Notes in
Economics and Mathematical Systems.
Springer-Verlag, Berlin, 1988.
Kurt Marti.
Stochastic optimization methods in engineering.
In System modelling and optimization (Prague, 1995), pages
75-87. Chapman & Hall, London, 1996.
Kurt Marti, editor.
Structural reliability and stochastic structural optimization.
Physica-Verlag, Heidelberg, 1997.
Math. Methods Oper. Res. 46 (1997), no. 3.
Kurt Marti.
Adaptive optimal stochastic trajectory planning and control
(AOSTPC) for robots.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Kurt Marti, editor.
Numerical methods for stochastic optimization and real-time
control of robots.
Gordon and Breach Science Publishers, Yverdon, 2000.
Selected papers from Section 14 on Stochastic Optimization of the
1998 Munich Stochastics Days and the DFG-Workshop on Real-time Control of
Robots held in Neubiberg/Munich, March 22-27, 1998, Optimization 47
(2000), no. 3-4.
Kurt Marti.
Stochastic optimization methods in robust adaptive control of robots.
In Online optimization of large scale systems, pages 545-577.
Springer, Berlin, 2001.
Kurt Marti.
Robust optimal design: a stochastic optimization problem.
In Stochastic optimization techniques (Neubiberg/Munich, 2000),
volume 513 of Lecture Notes in Econom. and Math. Systems, pages 35-55.
Springer, Berlin, 2002.
Kurt Marti, editor.
Stochastic optimization techniques, volume 513 of Lecture
Notes in Economics and Mathematical Systems.
Springer-Verlag, Berlin, 2002.
Numerical methods and technical applications, Including papers from
the 4th GAMM/IFIP Workshop held at the Federal Armed Forces University
Munich, Neubiberg/Munich, June 27-29, 2000.
Kurt Marti.
Stochastic Optimization Methods.
Springer, 2005.
Kurt Marti and Peter Kall, editors.
Stochastic programming, volume 423 of Lecture Notes in
Economics and Mathematical Systems, Berlin, 1995. Springer-Verlag.
Numerical techniques and engineering applications.
Kurt Marti and Peter Kall, editors.
Stochastic programming methods and technical applications,
Berlin, 1998. Springer-Verlag.
Olivier Martin, Steve W. Otto, and Edward W. Felten.
Large-step Markov chains for the TSP incorporating local
search heuristics.
Oper. Res. Lett., 11(4):219-224, 1992.
F. Martinelli.
Stochastic comparison algorithm for discrete optimization with
estimation of time-varying objective functions.
J. Optim. Theory Appl., 103(1):137-159, 1999.
Yukihiro Maruyama.
Positively bitone sequential decision process.
In Nonlinear analysis and convex analysis, pages 341-353.
Yokohama Publ., Yokohama, 2007.
J.L. Maryak.
Some guidelines for using iterate averaging in stochastic
approximation.
In Proceedings of the IEEE Conference on Decision and
Control, pages 2287-2290, 1997.
J.L. Maryak and D.C Chin.
Stochastic approximation for global random optimization.
In Proceedings of the American Control Conference, pages
3294-3298, 2000.
J.L. Maryak, R.H. Smith, and R.L. Winslow.
Modeling cardiac ion channel conductivity: model fitting via
simulation.
In Proceedings of the Winter Simulation Conference, eds. D.J.
Medeiros and E.F. Watson, pages 1587-1590, 1998.
S.F. Masri, G.A. Bekey, T.K. Caughey, and E. Van de Velde.
Adaptive stochastic optimization using multiprocessors.
Appl. Math. Comput., 72(2-3):225-257, 1995.
P. Massé.
Les Réserves et la Régulation de l'Avenir dans la vie
Économique.
Hermann, Paris, 1946.
vol I and II.
Rudolf Mathar and Antanas Zilinskas.
On global optimization in two-dimensional scaling.
Acta Appl. Math., 33(1):109-118, 1993.
Stochastic optimization.
V.John Mathews and Sung Ho Cho.
Improved convergence analysis of stochastic gradient adaptive
filters using the sign algorithm.
IEEE Trans. Acoust. Speech Signal Process. ASSP-35, 450-454,
1987.
Jirí Matousek.
Lower bounds for a subexponential optimization algorithm.
Random Structures Algorithms, 5(4):591-607, 1994.
T.H. Mattheiss and Brian K. Schmidt.
Computational results on an algorithm for finding all vertices of a
polytope.
Math. Programming, 18(3):308-329, 1980.
Josef Matyas.
Das zufaellige Optimierungsverfahren und seine Konvergenz. (A random
optimization method and her convergence).
In 5th internat. Analogue Comput. Meet. Lausanne 1967, Proc.,
540-544 , 1968.
Jerrold H. May and Robert L. Smith.
Random polytopes: their definition, generation and aggregate
properties.
Math. Programming, 24(1):39-54, 1982.
J. Mayer.
On the STABIL stochastic programming model.
Alkalmazott Mat. Lapok 2, 171-187, 1976.
J. Mayer.
A nonlinear programming method for the solution of a stochastic
programming model of A. Prékopa.
In Survey of mathematical programming (Proc. Ninth Internat.
Math. Programming Sympos., Budapest, 1976), Vol. 2, pages 129-139.
North-Holland, Amsterdam, 1979.
J. Mayer.
A nonlinear programming method for the solution of a stochastic
programming model of A. Prékopa.
Tanulmányok-MTA Számitástech. Automat. Kutató Int.
Budapest, 167:31-48, 1985.
Studies in applied stochastic programming, I.
J. Mayer.
Probabilistic constrained optimization-a brief summary on theory
and algorithms.
Investigación Oper., 14(2-3):162-174, 1993.
Workshop on Stochastic Optimization: the State of the Art (Havana,
1992).
J. Mayer.
On the numerical solution of stochastic optimization problems.
In System modeling and optimization, volume 199 of IFIP
Int. Fed. Inf. Process., pages 193-206. Springer, New York, 2006.
Janos Mayer.
Computational techniques for probabilistic constrained optimization
problems.
In Stochastic optimization. Numerical methods and technical
applications, Proc. GAMM/IFIP-Workshop, Neubiberg/Ger. 1990, Lect. Notes
Econ. Math. Syst. 379, 141-164, 1992.
János Mayer.
Stochastic linear programming algorithms.
Gordon and Breach Science Publishers, Amsterdam, 1998.
A comparison based on a model management system.
János Mayer.
On the numerical solution of jointly chance constrained problems.
In Probabilistic constrained optimization, volume 49 of
Nonconvex Optim. Appl., pages 220-235. Kluwer Acad. Publ., Dordrecht, 2000.
Vladimir Mazalov and Eduard Kochetov.
Mate choice and optimal stopping problem.
In Probability theory and mathematical statistics (Tokyo,
1995), pages 317-326. World Sci. Publishing, River Edge, NJ, 1996.
Vl.D. Mazurov.
The method of admissible corrections.
In Improper optimization problems, Collect. Artic., Sverdlovsk
1982, 11-22 , 1982.
Patrick H. McAllister.
Adaptive approaches to stochastic programming.
Ann. Oper. Res., 30(1-4):45-62, 1991.
Stochastic programming, Part I (Ann Arbor, MI, 1989).
Edward A. McBean.
Stochastic optimization in acid rain management with variable
meteorology.
In Stochastic optimization. Numerical methods and technical
applications, Proc. GAMM/IFIP-Workshop, Neubiberg/Ger. 1990, Lect. Notes
Econ. Math. Syst. 379, 165-172, 1992.
Richard P. McLean.
Approximation theory for stochastic variational and Ky Fan
inequalities in finite dimensions.
Ann. Oper. Res., 44(1-4):43-61, 1993.
Advances in equilibrium modeling, analysis and computation.
V.G. Medvedev.
An algorithm for solving a two-stage problem of linear stochastic
programming.
Vestnik Beloruss. Gos. Univ. Ser. I Fiz. Mat. Mekh., 1:64-66,
79, 1987.
M.V. Meerov and M.D. Vainstok.
Optimization of multiply-connected objects represented by a system
of regression equations as a stochastic-programming problem.
Autom. Remote Control 34, 1744-1748 translation from Avtom.
Telemekh. 1973, No.11, 36-41 (1973)., 1973.
Nicole Megow, Marc Uetz, and Tjark Vredeveld.
Models and Algorithms for Stochastic Online Scheduling.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Sanjay Mehortra and Gokhan Ozevin.
On the implementation of interior point decomposition algorithms for
two-stage stochastic conic.
Optimization Online, http://www.optimization-online.org, 2005.
Abraham Mehrez, Buddy L. Myers, and Moutaz J. Khouja.
Quality and inventory issues within the newsboy problem.
Comput. Oper. Res. 18, No.5, 397-410, 1991.
Abraham Mehrez and Moshe Sniedovich.
An analysis of a dynamic project cost problem.
J. Oper. Res. Soc. 43, No.6, 591-604, 1992.
Sanjay Mehrotra.
Volumetric center method for stochastic convex programs using
sampling.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Sanjay Mehrotra and M. Gokhan Ozevin.
Decomposition-based interior point methods for two-stage stochastic
convex quadratic programs with recourse.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Sanjay Mehrotra and M. Gökhan Özevin.
Two-stage stochastic semidefinite programming and decomposition based
interior point methods.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Aranyak Mehta, Scott Shenker, and Vijay V. Vazirani.
Posted price profit maximization for multicast by approximating fixed
points.
J. Algorithms, 58(2):150-164, 2006.
V.I. Melesko.
Ameliorated adaptation algorithms in problems with uncertainty based
upon pseudoinverse operators theory.
Izv. Akad. Nauk SSSR, Tekh. Kibernet. 1978, No.2, 39-52, 1978.
Jose Luis Menaldi.
Stochastic dynamic programming.
Cuad. Inst. Mat. "Beppo Levi" 16, 101 p., 1988.
Roy Mendelssohn.
Pareto optimal policies for harvesting with multiple objectives.
Math. Biosci. 51, 213-224, 1980.
Marta Susana Mendiondo and Richard H. Stockbridge.
Approximation of infinite-dimensional linear programming problems
which arise in stochastic control.
SIAM J. Control Optim., 36(4):1448-1472 (electronic), 1998.
Fan-wen Meng and Hui-fu Xu.
Exponential convergence of sample average approximation methods for a
class of stochastic mathematical programs with complementarity constraints.
J. Comput. Math., 24(6):733-748, 2006.
Fanwen Meng and Huifu Xu.
A regularized sample average approximation method for stochastic
mathematical programs with nonsmooth equality constraints.
SIAM J. Optim., 17(3):891-919 (electronic), 2006.
William Menke.
Geophysical data analysis: discrete inverse theory. Rev. ed.
International Geophysics Series 45. Academic Press, San Diego, CA
etc., 1989.
Carl-Heinz Meyer and Holger Knublauch.
Parallel iterative proportional fitting.
In Operations Research Proceedings 1999 (Magdeburg), pages
142-147, Berlin, 2000. Springer.
Sean P. Meyn.
Stability, performance evaluation, and optimization.
In Handbook of Markov decision processes, volume 40 of
Internat. Ser. Oper. Res. Management Sci., pages 305-346. Kluwer Acad.
Publ., Boston, MA, 2002.
Ph. Michel.
Programmes mathématiques mixtes. Application au principe du
maximum en temps discret dans le cas déterministe et dans le cas
stochastique.
RAIRO Rech. Opér., 14(1):1-19, 1980.
Maciej Michniewicz.
An application of the Kalman filter for optimization in the
presence of noise.
Arch. Automat. Telemech., 25(2):167-177, 1980.
Maciej Michniewicz.
On the application of static optimization methods to the
technological system control in the presence of noise.
Podstawy Sterowania 15, 315-334, 1985.
M.V. Mihalevic.
A stochastic search algorithm for the most preferred element of a
compact set.
Issled. Operatsii i ASU, 15:71-76, 135, 1980.
M.V. Mihalevic and L.B. Koslai.
Some questions of stability theory of the stochastic economical
models.
In Stability problems for stochastic models, Proc. 6th int.
Semin., Moscow 1982, Lect. Notes Math. 982, 123-135, 1983.
G. Mihoc and I. Nadejde.
Mathematical Programming: Parametric, Nonlinear and Stochastic.
Edutura Stiintifica, Bucurest, 1966.
(in Rumanian).
G.A. Mikhailov.
Minimax theory of Monte Carlo weight methods.
Zh. Vychisl. Mat. i Mat. Fiz., 24(9):1294-1302, 1984.
M.V. Mikhalevich.
Generalized stochastic method of centers.
Cybernetics, 16(2):292-296, 1980.
M.V. Mikhalevich.
Stability of stochastic programming methods to stochastic
quasigradient computing errors.
Cybernetics 16, 434-438 translation from Kibernetika 1980, No.3,
117-119 (1980)., 1981.
M.V. Mikhalevich.
Estimates for the convergence rate of stochastic methods of search
for the most preferable element.
Dokl. Akad. Nauk Ukr. SSR, Ser. A 1984, No.5, 74-76, 1984.
M.V. Mikhalevich.
Analysis of the error tolerance of stochastic methods to find the
most preferred element.
Cybernetics 21, 324-333 translation from Kibernetika 1985, No.3,
41-48 (1985)., 1985.
M.V. Mikhalevich.
Estimates for the rate of convergence of stochastic procedures using
averaged descent directions.
Kibernetika (Kiev), 2:91-100, 135, 1989.
M.V. Mikhalevich and L.B. Koshlaj.
Estimates of stability conditions for stochastic programming methods
and search methods for the most preferred element.
In Problems of stability of stochastic models, Proc. Semin.,
Moskva 1981, 81- 91, 1981.
M.V. Mikhalevich and L.B. Koshlaj.
Estimates and conditions for stability of stochastic programming
methods and methods of search for the most preferable element.
J. Sov. Math. 34, 1516-1524, 1986.
V.S. Mikhalevich, A.M. Gupal, and I.V. Bolgarin.
Numerical methods for solving nonstationary and limit extremal
problems without calculating the gradients.
Kibernetika (Kiev), 5:ii, 56-57, 65, 134, 1984.
V.S. Mikhalevich, A.M. Gupal, and V.I. Norkin.
Methods of non-convex optimization. (Metody nevypukloj
optimizatskii).
Ehkonomiko-Matematicheskaya Biblioteka. Moskva: Nauka, 1987.
V.S. Mikhalevich, I.V. Sergienko, and N.Z. Shor.
Investigation of optimization methods and their applications.
Cybernetics 17, 522-548 translation from Kibernetika 1981, No.4,
89-113 (1981)., 1982.
P. Milde.
Zu einer Problemstellung der stochastischen linearen Optimierung
Stochastische Zielfunktion.
In 18. internat. wiss. Kolloqu., Ilmenau 1, 55-57, 1973.
Peter Milde.
Zu einigen Problemstellungen der stochastischen linearen
Optimierung. (On some problems of stochastic linear programming).
Wiss. Z. Hochschule Architektur Bauwesen Weimar 18, 73-75,
1971.
Peter Milde.
Stochastische lineare Optimierung. I.
Wiss. Z. Hochschule Architektur Bauwesen Weimar 20, 531-535,
1973.
Yu.N. Minayev and N.M. Chumakov.
Algorithms for solving optimization problems under the influence of
noises.
Sov. Autom. Control 12, No.3, 56-61, 1979.
Hisashi Mine, Katsuhisa Ohno, and Masao Fukushima.
Decompostion of nonlinear chance-constrained programming problems by
dynamic programming.
J. Math. Anal. Appl., 56(1):221-222, 1976.
Julio MirasAmor.
Transporte mit zufaelliger Nachfrage.
Trabajos Estadist. Investig. operat. 24, Nr. 1/2, 185-197,
1973.
B.M. Mirkin and V.A. Sisljakova.
Optimization and analysis of the sensitivity of continuous stochastic
systems with discrete control.
In Optimization and adaptive control in large-scale systems
(Russian), pages 18-27, 105-106. "Ilim", Frunze, 1980.
Leonard J. Mirman.
One sector economic growth and uncertainty: A survey.
In Stochastic programming, Proc. int. Conf., Oxford 1974,
537-567, 1980.
Leonard J. Mirman and Daniel F. Spulber.
Fishery regulation with harvest uncertainty.
Int. Econ. Rev. 26, 731-746, 1985.
A. A. Mironov, A. A. Sokolov, and V. I. Tsurkov.
On the probabilistic properties of random transportation matrices
consisting of zeros and ones.
Izv. Akad. Nauk Teor. Sist. Upr., (6):102-113, 2001.
F. Mirozakhmedov and M.V. Mikhalevich.
Applied Aspects of Stochastic Programming.
Maorif, Dushanbe, 1989.
(in Russian).
F. Mirzoahmedov and V.L. Petrenko.
A dynamic problem of inventory planning with random demand.
In Methods in operations research and reliability theory in
systems of analysis (Russian), pages 89-94, 103, Kiev, 1979. Akad. Nauk
Ukrain. SSR Inst. Kibernet.
F. Mirzoahmedov and T.G. Timosenko.
An approach to the solution of the problem of control of the
operation of a single reservoir.
Akad. Nauk Ukrain. SSR Inst. Kibernet. Preprint, 13:45-51, 55,
1979.
Stochastic optimization models.
A.I. Mirzoakhmedov and A.I. Yastremskij.
Numerical solution methods and methods of economic and mathematical
analysis of stochastic resource planning problems. Methods of qualitative
analysis.
Issled. Oper. ASU 20, 3-9, 1982.
F. Mirzoakhmedov.
A three-stage problem of stochastic programming and the method of its
solution.
Dokl. Akad. Nauk Tadzhik. SSR, 23(12):694-697, 1980.
F. Mirzoakhmedov.
Methods of decomposition constructed on the basis of numerical
algorithms of stochastic programming.
Dokl. Akad. Nauk Tadzhik. SSR, 27(9):495-499, 1984.
F. Mirzoakhmedov.
Some numerical methods for solving two-stage problems of stochastic
programming.
Kibernetika (Kiev), 6:124-125, 1984.
F. Mirzoakhmedov.
An optimization problem in queueing theory and a numerical solution
method.
Cybernetics 26, No.3, 405-409 translation from Kibernetika 1990,
No.3, 73-75 (1990)., 1990.
F. Mirzoakhmedov.
Mathematical models and methods for production control taking
random factors into account. (Matematicheskie modeli i metody upravleniya
proizvodstvom s uchetom sluchajnykh faktorov).
Naukova Dumka, Kiev, 1991.
F. Mirzoakhmedov and A. Gafforov.
Stochastic penalty method for the solution of problems with
probabilistic constraints.
Dokl. Akad. Nauk Tadzhik. SSR, 26(12):761-763, 1983.
F. Mirzoakhmedov and I.N. Kurcheryavaya.
Projection algorithm on a multidimensional simplex.
Issled. Oper. ASU 23, 59-64, 1984.
F. Mirzoakhmedov and M.V. Mikhalevich.
Modeling of optimal cultivated land distribution by means of a
multistage stochastic programming problem.
Èkonom. i Mat. Metody, 18(5):918-922, 1982.
F. Mirzoakhmedov and M.V. Mikhalevich.
The stochastic quasigradient projection method.
Cybernetics 19, 566-575 translation from Kibernetika 1983, No.4,
103-109 (1983)., 1983.
F. Mirzoakhmedov, V.V. Mova, and L.V. Sirenko.
Numerical methods of stochastic programming in the optimization of
mass service systems.
Dokl. Akad. Nauk Tadzh. SSR 23, 625-628, 1980.
F. Mirzoakhmedov and V.L. Petrenko.
Stochastic programming problems associated with adaptive production
planning.
Cybernetics, 17(1):107-116, 1981.
F. Mirzoakhmedov and S.P. Uryas'ev.
Adaptive step regulation for a stochastic optimization algorithm.
Zh. Vychisl. Mat. i Mat. Fiz., 23(6):1314-1325, 1983.
F. Mirzoakhmedov and A.I. Yastremskii.
Numerical methods of solution and methods of mathematical-economic
analysis of stochastic problems of inventory planning. Methods of
qualitative analysis.
Issled. Operatsii i ASU, 20:3-9, 1982.
F. Mirzoakhmedov and A.I. Yastremskii.
Numerical methods of solution and methods of mathematical-economic
analysis of stochastic problems of inventory planning. Numerical methods of
solution.
Issled. Operatsii i ASU, 19:3-9, 1982.
F.M. Mirzoakhmedov and V.B. Barinov.
On a queueing problem.
Dokl. Akad. Nauk Tadzh. SSR 33, No.9, 590-592, 1990.
D. Mitra.
Analytical results on the use of reduced models in the control of
linear dynamical systems.
Proc. Inst. Elec. Engrs., 116:1439-1444, 1969.
Gautam Mitra, Harvey J. Greenberg, Freerk A. Lootsma, Marcel J. Rijckaert, and
Hans J. Zimmermann, editors.
Mathematical models for decision support. (Proceedings of the
NATO Advanced Study Institute held in Val d'Isere, France, July 26-August 6,
1987). NATO Scientific Affairs Division, Brussels (Belgium). NATO ASI
Series, F, 48. Berlin etc.: Springer-Verlag, 1988.
Yu. I. Mitrofanov and N. V. Yudaeva.
Methods for determining optimal routing parameters in queueing
networks.
Avtomat. i Telemekh., (8):109-117, 2001.
Sanjoy K. Mitter and Pal Toldalagi.
Variable metric methods and filtering theory.
In Recent developments in variable structure systems, economics
and biology (Proc. US-Italy Sem., Taormina, 1977), volume 162 of
Lecture Notes in Econom. and Math. Systems, pages 214-221. Springer,
Berlin, 1978.
Masakiyo Miyazawa.
The characterization of the stationary distribution of the
supplemented self-clocking jump process.
Math. Oper. Res., 16(3):547-565, 1991.
Norihiro Mizuno.
Generalized mathematical programming for optimal replacement in a
semi- Markov shock model.
Oper. Res. 34, 790-795, 1986.
Regina Hunter Mladineo.
Stochastic minimization of Lipschitz functions.
In Recent advances in global optimization (Princeton, NJ,
1991), Princeton Ser. Comput. Sci., pages 369-383. Princeton Univ. Press,
Princeton, NJ, 1992.
Mohammed Mnif and Huyên Pham.
Stochastic optimization under constraints.
Stochastic Process. Appl., 93(1):149-180, 2001.
Audrius Mockus, Jonas Mockus, and Linas Mockus.
Bayesian approach adapting stochastic and heuristic methods of global
and discrete optimization.
Informatica, 5(1-2):123-166, 1994.
I. Mockus.
Bayesian methods of search for an extremum.
In Advances in game theory (Proc. Second All-Union Conf.,
Vilnius, 1971) (Russian), pages 181-186. Izdat. "Mintis", Vilnius, 1973.
Ionas Mockus.
Sufficient conditions for the convergence of one-dimensional
Bayesian methods to the global minimum of continuous functions.
Teor. Optimal. Reshenii-Trudy Sem. Protsessy Optimal.
Upravleniya VI Sektsiya, 6:9-17, 151, 1980.
J. Mockus.
On Bayesian methods for seeking the extremal point.
In Stochastic programming, Proc. int. Conf., Oxford 1974,
287-299, 1980.
J. Mockus, W. Eddy, A. Mockus, L. Mockus, and G. Reklaitis.
Bayesian Heuristic Approach to Discrete and Global
Optimization.
Kluwer Academic Publishers, Dordrecht, The Netherlands, 1997.
J.B. Mockus.
On Bayesian methods in nondifferential and stochastic programming.
In Stochastic optimization, Proc. Int. Conf., Kiev/USSR 1984,
Lect. Notes Control Inf. Sci. 81, 475-486, 1986.
J.B. Mockus and L.J. Mockus.
Bayesian approach to global optimization and application to
multiobjective and constrained problems.
J. Optim. Theory Appl., 70(1):157-172, 1991.
Jonas Mockus.
On Bayesian methods for seeking the extremum and their application.
In Inf. Process. 77, Proc. IFIP Congr., Toronto 1977,
195-200, 1977.
Jonas Mockus.
Bayesian approach to global optimization, volume 37 of
Mathematics and its Applications (Soviet Series).
Kluwer Academic Publishers Group, Dordrecht, 1989.
Theory and applications, With a 5.25-inch IBM PC floppy disk.
Jonas Mockus.
Application of Bayesian approach to numerical methods of global and
stochastic optimization.
J. Global Optim., 4(4):347-365, 1994.
Jonas Mockus.
Average complexity and the Bayesian heuristic approach to discrete
optimization.
Informatica, 6(2):193-224, 1995.
Jonas Mockus.
A set of examples of global and discrete optimization.
Kluwer Academic Publishers, Dordrecht, 2000.
Applications of Bayesian heuristic approach.
Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, and Gintaras
Reklaitis.
Bayesian heuristic approach to discrete and global
optimization, volume 17 of Nonconvex Optimization and its
Applications.
Kluwer Academic Publishers, Dordrecht, 1997.
With 2 IBM-PC floppy-disks (3.5 inch; HD), Algorithms, visualization,
software, and applications.
Jonas Mockus and Henrikas Kuryla.
"Learning" Bayesian heuristics in flow-shop problem.
Informatica, 6(3):289-298, 1995.
Jonas Mockus, Audris Mockus, and Linas Mockus.
Bayesian approach for randomization of heuristic algorithms of
discrete programming.
In Randomization methods in algorithm design (Princeton, NJ,
1997), pages 161-177. Amer. Math. Soc., Providence, RI, 1999.
Jonas Mockus and Elvyra Senkien\.e.
Optimization of a "mixture" of pure heuristics and Monte Carlo
algorithms.
Informatica (Vilnius), 7(2):167-174, 1996.
Jonas B. Mockus.
The Bayesian approach to global optimization.
In Statistics: applications and new directions (Calcutta,
1981), pages 405-430. Indian Statist. Inst., Calcutta, 1984.
E. M. Modiano.
Derived demand and capacity planning under uncertainty.
Operations Research, 35(2):185-197, 1987.
L. Modica.
Stochastic homogenization and ergodic theory.
In Optimization and related fields (Erice, 1984), volume 1190
of Lecture Notes in Math., pages 359-370. Springer, Berlin, 1986.
R.H. Moehring, F.J. Radermacher, and G. Weiss.
Stochastic scheduling problems. I: General strategies.
Z. Oper. Res., Ser. A 28, 193-260, 1984.
R.H. Moehring, F.J. Radermacher, and G. Weiss.
Stochastic scheduling problems. II: Set strategies.
Z. Oper. Res., Ser. A 29, 65-104, 1985.
Rolf H. Moehring and Franz J. Radermacher.
Introduction to stochastic scheduling problems.
In Contributions to operations research, Proc. Conf.,
Oberwolfach/Ger. 1984, Lect. Notes Econ. Math. Syst. 240, 72-130, 1985.
C. Mohan and H. T. Nguyen.
A fuzzifying approach to stochastic programming.
Opsearch, 34(2):73-96, 1997.
C. Mohan and H. T. Nguyen.
An interactive satisficing method for solving multiobjective mixed
fuzzy-stochastic programming problems.
Fuzzy Sets and Systems, 117(1):61-79, 2001.
Theme: decision and optimization.
S. R. Mohan, S. K. Neogy, and T. Parthasarathy.
Pivoting algorithms for some classes of stochastic games: a survey.
Int. Game Theory Rev., 3(2-3):253-281, 2001.
Special issue on operations research and game theory with economic
and industrial applications (Chennai, 2000).
Ismail Bin Mohd.
Interval elimination method for stochastic spanning tree problem.
Appl. Math. Comput., 66(2-3):325-341, 1994.
V. V. Moiseenko and V. V. Yatskevich.
On global optimization in a class of stochastically unimodal
functions.
Kibernet. Sistem. Anal., (1):157-166, 190-191, 2000.
Wojciech Molisz.
Certain stochastic programming problems.
Arch. Automat. i Telemech., 19:17-25, 1974.
Andris Möller, Werner Römisch, and Klaus Weber.
Airline network revenue management by multistage stochastic
programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
N. P. Moloi and M. M. Ali.
An iterative global optimization algorithm for potential energy
minimization.
Comput. Optim. Appl., 30(2):119-132, 2005.
V.S. Molostvov.
Multiple-criteria optimization under uncertainty: Concepts of
optimality and sufficient conditions.
In Theory and practice of multiple criteria decision making,
Collect. Pap. Workshop, Moscow 1981, 91-105, 1983.
Howard M. Monroe and Jr. Sielken, Robert L.
Confidence limits for global optima based on heuristic solutions to
difficult optimization problems: a simulation study.
Amer. J. Math. Management Sci., 4(1-2):139-167, 1984.
Statistics and optimization : the interface.
Kee S. Moon, Farhad Azadivar, and Kyuil Kim.
A stochastic optimization of the production speed of robots based on
measured geometric and nongeometric errors.
Int. J. Prod. Res. 30, No.1, 49-62, 1992.
Chris Morey, John Scales, and Erik S. Van Vleck.
A feedback algorithm for determining search parameters for Monte
Carlo optimization.
J. Comput. Phys., 146(1):263-281, 1998.
Salvatore D. Morgera and Andrew C. Callahan.
Source-oriented adaptive beamforming.
Circuits Syst. Signal Process. 2, 487-516, 1983.
Hiroshi Morita and Hiroaki Ishii.
A stochastic improvement method for stochastic programming.
Comput. Statist. Data Anal., 14(4):477-487, 1992.
Hiroshi Morita and Hiroaki Ishii.
An efficient algorithm for a stochastic linear knapsack problem with
a single index model.
Math. Japon., 38(1):17-25, 1993.
Hiroshi Morita, Hiroaki Ishii, and Toshio Nishida.
Confidence region method for a stochastic programming problem.
J. Oper. Res. Soc. Japan, 30(2):218-231, 1987.
Hiroshi Morita, Hiroaki Ishii, and Toshio Nishida.
Confidence region method for a stochastic linear knapsack programming
problem.
Math. Japon., 33(4):559-564, 1988.
Hiroshi Morita, Hiroaki Ishii, and Toshio Nishida.
Stochastic programming with estimated objective.
Tech. Rep. Osaka Univ., 39(1947-1958):1-7, 1989.
Hiroshi Morita, Hiroaki Ishii, and Toshio Nishida.
Stochastic linear programming problem with partially estimated
constraint.
Math. Jap. 35, No.3, 551-559, 1990.
Hiroshi Morita and Lawrence M. Seiford.
Characteristics on stochastic DEA efficiency-reliability and
probability being efficient.
J. Oper. Res. Soc. Japan, 42(4):389-404, 1999.
William J. Morokoff.
Generating quasi-random paths for stochastic processes.
SIAM Rev., 40(4):765-788 (electronic), 1998.
P.A. Moroz.
Continuous optimization stochastic processes as idealized models of
procedures for global search optimization.
In Random search and pattern recognition (Russian), pages
50-66, 119. "Fan", Tashkent, 1985.
P.A. Moroz and A.P. Korostelev.
Ueber die Ermittlung des bedingten Extremums mit einem Zufalls-
Suchverfahren.
Avtomatika vycislit. Tehn., Riga 1975, Nr. 2, 36-39, 1975.
P.A. Moroz and A.P. Korostelev.
The principle of construction for stochastic optimization procedures
under constrained conditions.
Avtomat. i Vycisl. Tehn. (Riga), 3:37-42, 1976.
L.Ju. Morozenskii.
On the asymptotic length of a commercial traveller's path when towns
are randomly allocated.
Theory Probab. Appl. 19, 798-801 translation from Teor.
Verojatn. Primen. 19, 828-831 (1974)., 1974.
J.G. Morris and H.E. Thompson.
A note on the "value" of bounds on EVPI in stochastic programming.
Nav. Res. Logist. Q. 27, 165-169, 1980.
A.M. Morshedi.
An on-line search for optimum operating conditions of noisy
systems.
J. Franklin Inst. 310; 89-105, 1980.
D. Morton and E. Popova.
Monte carlo simulations for stochastic optimization.
In C.A. Floudas and P.M. Pardalos, editors, Encyclopedia of
Optimization. Kluwer Academic Publishers, 2001.
David P. Morton.
An enhanced decomposition algorithm for multistage stochastic
hydroelectric scheduling.
Ann. Oper. Res., 64:211-235, 1996.
Stochastic programming, algorithms and models (Lillehammer, 1994).
David P. Morton.
Stopping rules for a class of sampling-based stochastic programming
algorithms.
Oper. Res., 46(5):710-718, 1998.
David P. Morton and Guzin Bayraksan.
Assessing Solution Quality in Stochastic Programs.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
David P. Morton, Feng Pan, and Kevin J. Saeger.
Models for nuclear smuggling interdiction.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
David P. Morton, Javier Salmerón, and R. Kevin Wood.
A stochastic program for optimizing military sealift subject to
attack.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
David P. Morton and R. Kevin Wood.
Restricted-recourse bounds for stochastic linear programming.
Oper. Res., 47(6):943-956, 1999.
D.P. Morton, F. Pan, and K.J. Saeger.
Models for nuclear smuggling interdiction.
IIE Transactions on Operations Engineering, 38:3-14, 2007.
D.P. Morton and E. Popova.
A Bayesian stochastic programming approach to an employee
scheduling problem.
IIE Transactions on Operations Engineering, 36:155-167, 2003.
D.P. Morton, E. Popova, and I. Popova.
Efficient fund of hedge funds construction under downside risk
measures.
Journal of Banking and Finance, 30:503-518, 2006.
D.P. Morton, E. Popova, I. Popova, and M. Zhong.
Optimizing benchmark-based utility functions.
Bulletin of the Czech Econometric Society, 10:1-18, 2003.
D.P. Morton and R.K. Wood.
On a stochastic knapsack problem and generalizations.
In D.L. Woodruff, editor, Advances in Computational and
Stochastic Optimization, Logic Programming, and Heuristic Search: Interfaces
in Computer Science and Operations Research, pages 149-168. Kluwer Academic
Publishers, Boston, 1998.
M. Mostaghimi.
Monetary policy simulation using SPSA-based neural networks.
In Proceedings of the IEEE Conference on Decision and
Control, pages 492-497, 1997.
I. B. Motskus.
Bayesian methods of optimization of continuous functions, computed
with errors.
In Mathematical models in immunology and medicine (Russian)
(Moscow, 1982), pages 257-261. "Mir", Moscow, 1986.
Ionas Motskus and Linas Motskus.
A Bayesian approach to global optimization, and applications.
Teor. Optimal. Reshenii, 12:54-70, 1987.
Ionas Motskus, Lolita Zhukauskaite, and Tautvidas Lideikis.
A Bayesian approach to local optimization.
Teor. Optimal. Reshenii, 12:71-79, 1987.
Jonas Motskus.
Sufficient conditions for the convergence of Bayesian methods to the
global minimum.
In Numer. Optim. Meth. Appl., Optim. Decis. Theory, Issue 4,
Inst. Math. Cybern. Acad. Sci. Lith. SSR, 67-88, 1978.
P.H. Mueller.
Probleme der Steuerung stochastischer Prozesse.
Mitt. Math. Ges. DDR 1977, No.1, 57-74, 1977.
P.Heinz Mueller and Eckhard Platen.
Das asymptotische Verhalten optimaler Rangstrategien beim "Problem
der besten Wahl".
Wiss. Z. Tech. Univ. Dres. 24, 933-937, 1975.
Rahul Mukerjee.
Univariate stochastic programming with random decision variable.
J. Oper. Res. Soc., 33(10):957-959, 1982.
Eh.A. Mukhacheva, I.A. Solomeshch, and N.I. Solomeshch.
Optimization of linear stochastic cut-out according to the cost
criterion.
Optimizatsiya 44(61), 56-63, 1988.
Eh.A. Mukhacheva and N.I. Solomeshch.
The problem of stochastic linear cut-out.
Optimizatsiya 36(53), 49-55, 1985.
S.P. Mukherjee.
Mixed strategies in chance-constrained programming.
J. Oper. Res. Soc., 31(11):1045-1047, 1980.
P. Heinz Müller, Andreas Hahnewald-Busch, Peter Neumann, and Volker Nollau.
Optimierung mittels stochastischer Suchverfahren.
Wiss. Z. Tech. Univ. Dresden, 32(1):69-75, 1983.
Peter Müller.
Simulation-based optimal design.
In Bayesian statistics, 6 (Alcoceber, 1998), pages 459-474.
Oxford Univ. Press, New York, 1999.
Peter Müller and Giovanni Parmigiani.
Optimal design via curve fitting of Monte Carlo experiments.
J. Amer. Statist. Assoc., 90(432):1322-1330, 1995.
John M. Mulvey and Andrzej Ruszczy\'nski.
A diagonal quadratic approximation method for linear multistage
stochastic programming problems.
In System modelling and optimization (Zurich, 1991), volume 180
of Lecture Notes in Control and Inform. Sci., pages 588-597. Springer,
Berlin, 1992.
John M. Mulvey and Andrzej Ruszczy\'nski.
A new scenario decomposition method for large-scale stochastic
optimization.
Oper. Res., 43(3):477-490, 1995.
John M. Mulvey, Robert J. Vanderbei, and Stavros A. Zenios.
Robust optimization for large-scale systems.
Operations Research, 43:264-281, 1995.
John M. Mulvey and Hercules Vladimirou.
Evaluation of a parallel hedging algorithm for stochastic network
programming.
In R. Sharda, B. L. Golden, E. Wasil, O. Balci, and W. Stewart,
editors, Impacts of Recent Computer Advances on Operations Research,
pages 106-119. North-Holland, New York, 1989.
John M. Mulvey and Hercules Vladimirou.
Stochastic network optimization models for investment planning.
Ann. Oper. Res. 20, 187-217, 1989.
John M. Mulvey and Hercules Vladimirou.
Applying the progressive hedging algorithm to stochastic generalized
networks.
Ann. Oper. Res., 31(1-4):399-424, 1991.
Stochastic programming, Part II (Ann Arbor, MI, 1989).
John M. Mulvey and Hercules Vladimirou.
Solving multistage stochastic networks: An application of scenario
generation.
Networks, 21:619-643, 1991.
John M. Mulvey and Hercules Vladimirou.
Stochastic network programming for financial planning problems.
Manage. Sci. 38, No.11, 1642-1664, 1992.
Cécile Murat and Vangelis Th. Paschos.
Problème du stable probabiliste.
C.R. Acad. Sci. Paris Sér. I Math., 321(4):495-498, 1995.
Cécile Murat and Vangelis Th. Paschos.
The probabilistic minimum vertex-covering problem.
Int. Trans. Oper. Res., 9(1):19-32, 2002.
E.I. Murga and N.V. Roenko.
The direct method for solution and analysis of a two-stage linear
stochastic problem.
In Methods and software for optimization, modeling and
construction of systems, Collect. Sci. Works, Kiev, 73-81, 1988.
Yoshisada Murotsu and Fuminori Oba.
A study on linear programming under uncertainty.
Bull. Univ. Osaka Prefecture Ser. A, 23:61-74, 1974.
Yoshisada Murotsu, Fuminori Ohba, and Tsutomu Abe.
A method of determining tolerances for the control variables in
stochastic linear programming problems.
Systems and Control, 18:219-225, 1974.
Yoshisada Murotsu, Fuminori Ohba, and Hiroshi Itoh.
On a solution of stochastic nonlinear programming problems.
Bull. Univ. Osaka Prefecture Ser. A, 20(1):97-108, 1971.
Robert A. Murphey.
An approximate algorithm for a weapon target assignment stochastic
program.
In Approximation and complexity in numerical optimization
(Gainesville, FL, 1999), volume 42 of Nonconvex Optim. Appl., pages
406-421. Kluwer Acad. Publ., Dordrecht, 2000.
F.H. Murphy, S. Sen, and A.L. Soyster.
Electric utility capacity expansion planning with uncertain load
forecasts.
AIIE Transaction 14:52-59, 1982.
Michael R. Murr and András Prékopa.
Solution of a product substitution problem using stochastic
programming.
In Probabilistic constrained optimization, volume 49 of
Nonconvex Optim. Appl., pages 252-271. Kluwer Acad. Publ., Dordrecht, 2000.
D.A. Murtazin and A.S. Poznyak.
Recurrent algorithms for search optimization under conditions of
relative noise. I. Limit possibilities.
Avtomat. i Telemekh., 9:95-109, 1987.
D.A. Murtazin and A.S. Poznyak.
Recurrent algorithms for search optimization under conditions of
relative noise. II. Optimal search procedures.
Avtomat. i Telemekh., 10:89-96, 1987.
D.A. Murtazin and A.S. Poznyak.
Recurrent search optimization algorithms in the presence of relative
noise. II: Optimal search procedures.
Autom. Remote Control 48, No.10, 1343-1349 translation from
Avtom. Telemekh. 1987, No.10, 89-96 (1987)., 1987.
Raymond Nadeau and Radu Theodorescu.
Restricted Bayes strategies for programs with simple recourse.
Oper. Res., 28(3, part 2):777-784, 1980.
Raymond Nadeau and Radu Theodorescu.
Restricted Bayes strategies for convex stochastic programs.
Rend. Circ. Mat. Palermo (2), 33(1):109-116, 1984.
Raymond Nadeau and Bruno Urli.
A fuzzy/stochastic multiobjective linear programming method.
In Multiple criteria decision making, Proc. 9th Int. Conf.
Theory Appl. Bus. Ind. Gov., Fairfax/VA (USA) 1990, 283-296, 1992.
Raymond Nadeau, Bruno Urli, and Laszlo N. Kiss.
PROMISE: A DSS for multiple objective stochastic linear programming
problems.
Ann. Oper. Res. 51, 45-59, 1994.
Tamás Nagy.
Stochastic variants of the entropy programming.
Alkalmaz. Mat. Lapok, 17(1-2):143-169 (1994), 1993.
Tamás Nagy.
A stochastic variant of the transportation problem.
Investigación Oper., 15(2-3):191-205, 1994.
Tamás Nagy.
Models and algorithms of the entropy programming.
Alkalmaz. Mat. Lapok, 22(1):115-134, 2005.
Dania Naja.
Convergence faible du recuit simulé généralisé à temps
discret.
C. R. Acad. Sci. Paris Sér. I Math., 328(12):1213-1218,
1999.
K. Najim, A. Rusnak, A. Meszaros, and M. Fikar.
Constrained long-range predictive control based on artificial neural
networks.
International Journal of Systems Science, 28:1211-1226, 1997.
T¯oru Nakai.
An optimal assignment problem for multiple objects per period-case
of a partially observable Markov chain.
Bull. Inform. Cybernet., 31(1):23-34, 1999.
Masao Nakamura.
On a class of stochastic optimization problems with a specified
growth pattern.
Management Sci., Appl. 20, 236-239, 1973.
A.N. Nakonechnyi.
Optimal replacement of systems with respect to turnout.
In Mathematical methods for the analysis and optimization of
complex systems that function under conditions of uncertainty (Russian),
pages 33-40, iii. Akad. Nauk Ukrain. SSR Inst. Kibernet., Kiev, 1986.
A.N. Nakonechnyi.
The method of the small parameter in the analysis of stochastic
extremal problems with rare events.
In Analysis of stochastic systems by methods in operations
research and reliability theory (Russian), pages 29-31, iii. Akad. Nauk
Ukrain. SSR Inst. Kibernet., Kiev, 1987.
A.N. Nakonechnyi.
Singularly perturbed stochastic extremal problems.
In Mathematical methods for the analysis of complex stochastic
systems (Russian), pages 38-44, iii-iv, Kiev, 1988. Akad. Nauk Ukrain. SSR
Inst. Kibernet.
A.N. Nakonechnyi.
On the method of successive random approximations.
Dokl. Akad. Nauk Ukrain. SSR Ser. A, 9:16-18, 85, 1989.
A.N. Nakonechnyi.
Extremal problems with rare events. I.
Kibernetika (Kiev), 5:55-58, 66, 133, 1990.
A.N. Nakonechnyi.
Extremal problems with rare events. II. The method of
successive random approximations.
Kibernet. Sistem. Anal., 2:33-39, 188, 1992.
A.N. Nakonechnyi.
Iterative processes: a survey of the theory of convergence that uses
Lyapunov's second method.
Kibernet. Sistem. Anal., 4:66-85, 189, 1994.
A.N. Nakonechnyi.
A probability-theoretic analogue of the method of vector Lyapunov
functions.
Kibernet. Sistem. Anal., 3:110-119, 191, 1994.
A.N. Nakonechnyj.
On the method of succesive random approximations.
Dokl. Akad. Nauk Ukr. SSR, Ser. A 1989, No.9, 16-18, 1989.
P. Nandakumar, S.M. Datar, and R. Akella.
Models for measuring and accounting for cost of conformance
quality.
Manage. Sci. 39, No.1, 1-16, 1993.
Hiroyuki Narihisa.
An algorithm for solving the stochastic programming problem.
Mem. Defense Acad., 18(3):151-160, 1978.
Mohammad Naseh and M. M. Khalid.
Solution of chance constrained linear programming by ellipsoid
method.
Pure Appl. Math. Sci., 59(1-2):71-80, 2004.
P. Nash and R.R. Weber.
Dominant strategies in stochastic allocation and scheduling
problems.
In Deterministic and stochastic scheduling, Proc. NATO Adv.
Study Res. Inst., Durham/Engl. 1981, 343-353, 1982.
J. L. Nazareth.
The d\sb lp decision support system and its extension
to stochastic programming.
Optim. Methods Softw., 13(2):117-154, 2000.
J. L. Nazareth.
The D\sb LP decision support system and its extension to
stochastic programming.
Optim. Methods Softw., 13(2):117-154, 2000.
J.L. Nazareth.
Algorithms based upon generalized linear programming for stochastic
programs with recourse.
In Stochastic programming (Gargnano, 1983), volume 76 of
Lecture Notes in Control and Inform. Sci., pages 210-234. Springer, Berlin,
1986.
J.L. Nazareth and R.J.-B. Wets.
Algorithms for stochastic programs: the case of nonstochastic
tenders.
Math. Programming Stud., 28:1-28, 1986.
Stochastic programming 84. II.
L. Nazareth.
Design and implementation of a stochastic programming optimizer with
recourse and tenders.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 273-294. Springer, Berlin, 1988.
L. Nazareth and R.J.-B. Wets.
Nonlinear programming techniques applied to stochastic programs with
recourse.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 95-121. Springer, Berlin, 1988.
A.V. Nazin.
Game approach to the solution of a problem of stochastic programming.
Automat. Remote Control, 39(2, part 1):215-223, 1978.
A.V. Nazin.
Game problem of adaptive selection of versions and an algorithm for
solving it.
Autom. Remote Control 45, 1443-1448 translation from Avtom.
Telemekh. 1984, No.11, 70-75 (1984)., 1984.
A.V. Nazin.
Informational inequalities in the problem of gradient stochastic
optimization and optimal realizable algorithms.
Autom. Remote Control 50, No.4, 531-540 translation from Avtom.
Telemekh. 1989, No.4, 127-138 (1989)., 1989.
T.V. Necaeva.
Ueber die Moeglichkeit der Reduktion des Problems der kollektiven
Entscheidungen zum Mehretappenproblem der stochastischen Programmierung.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1975, Nr. 3, 30-36, 1975.
T.V. Nechayeva.
The possibility of reducing a problem of collective decisions to a
many-step stochastic programming problem.
Engrg. Cybernetics, 13(3):25-31, 1975.
N.A. Nechval'.
On a method of stochastic optimization.
Probl. Sluchajnogo Poiska 4, 85-94, 1975.
N.A. Nechval'.
The synthesis of adaptive control strategies for successive
processes in problems of the statistical search for the extremum and in the
theory of taking statistical decisions.
Probl. Sluchajnogo Poiska 8, 186-236, 1980.
M.C. Nechyba and Y. Xu.
Human-control strategy: abstraction, verification, and replication.
IEEE Control Systems Magazine, 17(5):48-61, 1997.
V.N. Nefëdov.
Calculating a probabilistic optimization criterion by branch and cut
methods.
J. Comput. Systems Sci. Internat., 32(6):62-71, 1994.
C.V. Negoita, P. Flondor, and M. Sularia.
On fuzzy environment in optimization problems.
Econ. Comput. Econ. Cybern. Stud. Res. 1977, 13-24, 1977.
C.V. Negoi ta and M. Sularia.
Optimization in fuzzy environment.
In Digital processing and transmission of data and control of
processes by means of computers, volume 10 of Probleme de Automat.,
pages 241-251. Ed. Acad. R.S. România, Bucharest, 1978.
Frederike Neise.
Optimization of dispersed generation systems including risk aversion.
In CTW2006-Cologne-Twente Workshop on Graphs and Combinatorial
Optimization, volume 25 of Electron. Notes Discrete Math., pages
105-106 (electronic). Elsevier, Amsterdam, 2006.
V.V. Nekrutkin and A.S. Tikhomirov.
Speed of convergence as a function of given accuracy for random
search methods.
Acta Appl. Math., 33(1):89-108, 1993.
Stochastic optimization.
Z.V. Nekrylova.
Ueber die Methode des stochastischen Gradienten im
unendlich-dimensionalen Raume.
Kibernetika, Kiev 1974, Nr. 1, 142-144, 1974.
Z.V. Nekrylova.
Ueber die Konvergenzgeschwindigkeit der Methode des stochastischen
Gradienten.
Kibernetika, Kiev 1975, Nr. 2, 40-43, 1975.
Z.V. Nekrylova.
Asymptotic behavior of the stochastic quasigradient method.
Kibernetika (Kiev), 3:81-85, 1978.
Z.V. Nekrylova.
A method for estimating the unknown parameters of the functional
dependence between variables which are observed with noise.
Kibernetika (Kiev), 4:61-63, 135, 1987.
Arkadi Nemirovski and Alexander Shapiro.
Scenario approximations of chance constraints.
Optimization Online, http://www.optimization-online.org, 2004.
Arkadi Nemirovski and Alexander Shapiro.
Convex approximations of chance constrained programs.
SIAM J. Optim., 17(4):969-996 (electronic), 2006.
Arkadi Nemirovski and Alexander Shapiro.
On complexity of shmoys - swamy class of two-stage linear stochastic
programming problems.
Optimization Online, http://www.optimization-online.org, 2006.
A.S. Nemirovskii, B.T. Polyak, and Ya. Z. Tsypkin.
Optimal algorithms for stochastic optimization under multiplicative
noise.
Dokl. Akad. Nauk SSSR, 284(3):564-567, 1985.
A.S. Nemirovskij, B.T. Polyak, and Ya.Z. Tsypkin.
Optimum algorithms for stochastic optimization with multiplicative
error.
Sov. Phys., Dokl. 30, No.9, 744-745 translation from Dokl. Akad.
Nauk SSSR 284, No.3 (1988)., 1988.
Yu. Nesterov.
Characteristic functions of directed graphs and applications to
stochastic equilibrium problems.
Optim. Eng., 8(2):193-214, 2007.
Yu. Nesterov and J.-Ph. Vial.
Confidence level solutions for stochastic programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Aurel Netea.
Sur un probleme de programmation lineaire aleatoire
pseudo-booleenne.
In Operations research, Proc. 3rd Colloq., Cluj-Napoca/Rom.
1978, 194-198 , 1979.
Serguei Netessine, Gregory Dobson, and Robert A. Shumsky.
Flexible service capacity: optimal investment and the impact of
demand correlation.
Oper. Res., 50(2):375-388, 2002.
Dan Nettleton.
Convergence properties of the EM algorithm in constrained
parameter spaces.
Canad. J. Statist., 27(3):639-648, 1999.
K. Neumann.
Dynamische Optimierung und optimale Steuerung in speziellen GERT-
Netzplaenen.
Wiss. Z., Tech. Hoch-sch. Leipz. 4, 349-355, 1980.
K. Neumann.
An optimality equation for stochastic decision networks.
Wiss. Z., Tech. Hochsch. Leipz. 8, 79-87, 1984.
Klaus Neumann.
Optimal time-cost trade-offs by means of stochastic networks.
Methods Oper. Res. 42, 5-16, 1981.
Klaus Neumann.
GERT networks with tree structure: Properties, temporal analysis,
cost minimization, and scheduling.
In Contributions to operations research, Proc. Conf.,
Oberwolfach/Ger. 1984, Lect. Notes Econ. Math. Syst. 240, 142-158, 1985.
H. Niederreiter.
A quasi Monte Carlo method for the approximate computation of the
extreme values of a function.
In Studies in pure mathematics, Mem. of P. Turan, 523-529,
1983.
H. Niederreiter and P. Peart.
A comparative study of quasi - Monte Carlo methods for optimization
of functions of several variables.
Caribb. J. Math. 1, 27-44, 1982.
Harald Niederreiter.
Quasi-Monte Carlo methods for global optimization.
In Mathematical statistics and applications, Vol. B (Bad
Tatzmannsdorf, 1983), pages 251-267. Reidel, Dordrecht, 1985.
Soren S. Nielsen and Stavros A. Zenios.
An investigation of the effect of problem structure on stochastic
programming algorithms.
In Dongarra, Jack (ed.) et al., Proceedings of the fifth SIAM
conference on parallel processing for scientific computing, held in Houston,
TX, USA, March 25-27, 1991. Philadelphia, PA: SIAM, (ISBN 0-89871-303-X/pbk).
193- 198, 1992.
Soren S. Nielsen and Stavros A. Zenios.
A massively parallel algorithm for nonlinear stochastic network
problems.
Oper. Res. 41, No.2, 319-337, 1993.
Soren S. Nielsen and Stavros A. Zenios.
Solving multistage stochastic network programs on massively parallel
computers.
Math. Programming (Ser. A), 73(3):227-250, 1996.
Soren S. Nielsen and Stavros A. Zenios.
A stochastic programming model for funding single premium deferred
annuities.
Math. Programming (Ser. B), 75(2):177-200, 1996.
Approximation and computation in stochastic programming.
Soren S. Nielsen and Stavros A. Zenios.
Scalable parallel Benders decomposition for stochastic linear
programming.
Parallel Comput., 23(8):1069-1088, 1997.
Wojciech Niemiro.
Limit distributions of simulated annealing Markov chains.
Discuss. Math. Algebra Stochastic Methods, 15(2):241-269,
1995.
Wojciech Niemiro.
Tail events of simulated annealing Markov chains.
J. Appl. Probab., 32(4):867-876, 1995.
Peter Nijkamp and Aura Reggiani.
Interaction, evolution and chaos in space.
Springer-Verlag, Berlin, 1992.
E.G. Nikolaev.
Low descent by means of random m-gradient method.
Avtomat. vycislit. Tehn., Riga 1970, No.3, 40-46, 1970.
N.D. Nikolaeva.
A certain stochastic programming problem.
In Mathematical methods for the solution of economic problems
(Suppl. to Ékonom. i Mat. Metody, Collection No. 3) (Russian), pages
52-58. Izdat. "Nauka", Moscow, 1972.
Efstratios Nikolaidis and Ricardo Burdisso.
Reliability based optimization: A safety index approach.
Comput. Struct. 28, No.6, 781-788, 1988.
José Niño-Mora.
Marginal productivity index policies for scheduling restless bandits
with switching penalties.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
N.N., editor.
Optimization theory and related fields. Proceedings of a
symposium held at the Research Institute for Mathematical Sciences, Kyoto
University, Kyoto, Japan, November 5-7, 1990. RIMS Kokyuroku. 747. Kyoto:
Kyoto University, Research Institute for Mathematical Sciences, iv, 197 p.,
1991.
Marie-Cécile Noël and Yves Smeers.
On the use of nested decomposition for solving nonlinear multistage
stochastic programs.
In Stochastic programming (Gargnano, 1983), volume 76 of
Lecture Notes in Control and Inform. Sci., pages 235-246. Springer, Berlin,
1986.
Marie-Cécile Noël and Yves Smeers.
Nested decomposition of multistage nonlinear programs with recourse.
Math. Programming, 37(2):131-152, 1987.
V. Nollau.
A stochastic decision model with vector-valued reward.
Optimization 16, 733-742, 1985.
V. Nollau and A. Hahnewald-Busch.
An approximation procedure for stochastic dynamic programming based
on clustering of state and action spaces.
Math. Operationsforsch. Statist. Ser. Optim., 10(1):121-130,
1979.
Volker Nollau.
Some aspects of vector-valued stochastic dynamic programming.
In Markov processes and control theory (Gaußig, 1988),
volume 54 of Math. Res., pages 149-157. Akademie-Verlag, Berlin, 1989.
V. I. Norkin and B. O. Onishchenko.
Minorant methods of stochastic global optimization.
Kibernet. Sistem. Anal., 41(2):56-70, 189, 2005.
V.I. Norkin.
Two random search algorithms for the minimization of
nondifferentiable functions.
In Mathematical methods in operations research and reliability
theory (Russian), pages 36-40, Kiev, 1978. Akad. Nauk Ukrain. SSR Inst.
Kibernet.
V.I. Norkin.
Random generalized-differential functions in the problem of nonconvex
nonsmooth stochastic optimization.
Kibernetika (Kiev), 6:98-102, 135, 1986.
V.I. Norkin.
Random Lipschitz functions.
Kibernetika (Kiev), 76(2):66-71, 1986.
V.I. Norkin.
Stochastic generalized-differentiable functions in the problem of
nonconvex nonsmooth stochastic optimization.
Cybernetics 22, No.6, 804-809 translation from Kibernetika 1986,
No.6, 98-102 (1986)., 1986.
V.I. Norkin.
Optimizatsiya veroyatnostei, volume 89 of
Preprint.
Akad. Nauk Ukrain. SSR Inst. Kibernet., Kiev, 1989.
V.I. Norkin.
Ustoichivost' stokhasticheskikh optimizatsionnykh
modelei i statisticheskie metody stokhasticheskogo programmirovaniya (po
materialam IIASA), volume 89 of Preprint.
Akad. Nauk Ukrain. SSR Inst. Kibernet., Kiev, 1989.
V.I. Norkin.
Conditions for and convergence rate of the method of empirical
averages in mathematical statistics and stochastic programming.
Kibernet. Sistem. Anal., 2:107-120, 190, 1992.
V.I. Norkin, Yu.M. Ermoliev, and A. Ruszczynski.
On optimal allocation of indivisibles under uncertainty.
Operations Research, 46(3):381-395, 1998.
Vladimir Norkin.
Global optimization of probabilities by the stochastic branch and
bound method.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 186-201. Springer, Berlin, 1998.
Vladimir Norkin and Boris. Onischenko.
Minorant methods for stochastic global optimization.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Vladimir I. Norkin, Georg Ch. Pflug, and Andrzej Ruszczy\'nski.
A branch and bound method for stochastic global optimization.
Math. Programming, 83(3, Ser. A):425-450, 1998.
Alfred L. Norman and David W. Shimer.
Risk, uncertainty, and complexity.
J. Econ. Dyn. Control 18, No.1, 231-249, 1994.
D.A. Novikov.
Optimal stimulation mechanisms in an active system with probabilistic
uncertainty. III.
Avtomat. i Telemekh., 12:118-123, 1995.
N.M. Novikova.
A stochastic quasigradient method for maximin search.
Z. Vycisl. Mat. i Mat. Fiz., 17(1):91-99, 284, 1977.
N.M. Novikova.
A stochastic quasi-gradient method of solving optimization problems
in Hilbert space.
U.S.S.R. Comput. Math. Math. Phys. 24, No.2, 6-16, 1984.
N.M. Novikova.
Some methods of stochastic programming in Hilbert space.
U.S.S.R. Comput. Math. Math. Phys. 25, No.6, 127-138 translation
from Zh. Vychisl. Mat. Mat. Fiz. 25, No.12, 1795-1813 (1985)., 1985.
N.M. Novikova.
On stochastic programming in Hilbert space.
In Stochastic optimization (Kiev, 1984), volume 81 of
Lecture Notes in Control and Inform. Sci., pages 487-495. Springer, Berlin,
1986.
N.M. Novikova.
A method of finding a stochastic saddle point.
U.S.S.R. Comput. Math. Math. Phys. 29, No.1, 27-34 translation
from Zh. Vychisl. Mat. Mat. Fiz. 29, No.1, 39-49 (1989)., 1989.
N.M. Novikova.
Some methods for the numerical solution of continuous convex
stochastic problems of optimal control.
Zh. Vychisl. Mat. i Mat. Fiz., 31(11):1605-1618, 1991.
N.M. Novikova.
Iterative stochastic methods for solving variational problems of
mathematical physics and operations research.
J. Math. Sci., 68(1):1-124, 1994.
Analysis, 2.
A. Nowak.
On a general dynamic programming problem.
Colloq. Math., 37(1):131-138, 1977.
Andrzej Nowak.
Random Kuhn-Tucker theorem.
In Transactions of the ninth Prague conference on information
theory, statistical decision functions, random processes, Vol. B (Prague,
1982), pages 95-97. Reidel, Dordrecht, 1983.
Andrzej S. Nowak.
Stationary equilibria for non-zero-sum average payoff ergodic
stochastic games with general state space.
In Advances in dynamic games and applications (Geneva, 1992),
volume 1 of Ann. Internat. Soc. Dynam. Games, pages 231-246.
Birkhäuser Boston, Boston, MA, 1994.
Maciej Nowak.
Aspiration level approach in stochastic MCDM problems.
European J. Oper. Res., 177(3):1626-1640, 2007.
Matthias P. Nowak and Werner Römisch.
Stochastic Lagrangian relaxation applied to power scheduling in a
hydro-thermal system under uncertainty.
Ann. Oper. Res., 100:251-272 (2001), 2000.
Research in stochastic programming (Vancouver, BC, 1998).
Matthias P. Nowak, Rüdiger Schultz, and Markus Westphalen.
Optimization of simultaneous power production and trading by
stochastic integer programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2002.
Matthias P. Nowak, Rüdiger Schultz, and Markus Westphalen.
A stochastic integer programming model for incorporating day-ahead
trading of electricity into hydro-thermal unit commitment.
Optim. Eng., 6(2):163-176, 2005.
M.P. Nowak, R. Nürnberg, W. Römisch, R. Schultz, and M. Westphalen.
Stochastic programming for power production and trading under
uncertainty.
Preprint SM-DU-471, Fachbereich Mathematik, Universität Duisburg,
2000.
submitted.
M.P. Nowak and W. Römisch.
Stochastic lagrangian relaxation applied to power scheduling in a
hydro-thermal system under uncertainty.
Annals of Operations Research, 100, 2001.
to appear.
Nilay Noyan, Gábor Rudolf, and Andrzej Ruszczy\'nski.
Relaxations of linear programming problems with first order
stochastic dominance constraints.
Oper. Res. Lett., 34(6):653-659, 2006.
Nilay Noyan and Andrzej Ruszczynski.
Valid inequalities and restrictions for stochastic programming
problems with first order stochastic dominance constraints.
Optimization Online, http://www.optimization-online.org, 2006.
Frantisek Nozicka.
über einfache Mannigfaltigkeiten in linearem affinen Raum
A\sbn in globaler Auffassung.
Czechoslovak Math. J., 26(101)(4):541-578, 1976.
Lewis Ntaimo and Suvrajeet Sen.
The million-variable "march" for stochastic combinatorial
optimization.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Lewis Ntaimo and Suvrajeet Sen.
A comparative study of decomposition algorithms for stochastic
combinatorial optimization.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Lewis Ntaimo and Suvrajeet Sen.
The million-variable "march" for stochastic combinatorial
optimization.
J. Global Optim., 32(3):385-400, 2005.
Lewis Ntaimo and Suvrajeet Sen.
A branch-and-cut algorithm for two-stage stochastic mixed-binary
programs with continuous first-stage variables.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
Lewis Ntaimo and Matthew W. Tanner.
Computations with disjunctive cuts for two-stage stochastic mixed 0-1
integer programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2007.
E.A. Nurminskii.
Conditions for the convergence of stochastic programming algorithms.
Kibernetika (Kiev), 3:84-87, 1973.
E.A. Nurminskii.
Chislennye metody resheniya determinirovannykh i
stokhasticheskikh minimaksnykh zadach.
"Naukova Dumka", Kiev, 1979.
E.A. Nurminskij.
Numerical methods of the solution of deterministic and
stochastic minimax problems. (Chislennye metody resheniya determinirovannykh
i stokhasticheskikh minimaksnykh zadach.).
Akademiya Nauk Ukrainskoj SSR, Ordena Lenina Institut Kibernetiki.
Kiev: "Naukova Dumka"., 1979.
E. M. Oblow.
SPT: a stochastic tunneling algorithm for global optimization.
J. Global Optim., 20(2):195-212, 2001.
Ralf Oestermark.
A chance-constraint programming approach to the capital pricing
model.
Kybernetes 20, No.5, 42-49, 1991.
Wlodzimierz Ogryczak and Andrzej Ruszczy\'nski.
Dual stochastic dominance and related mean-risk models.
SIAM J. Optim., 13(1):60-78 (electronic), 2002.
James A. Ohlson.
Quadratic approximations of the portfolio selection problem when the
means and variances of returns are infinite.
Management Sci. 23, 576-584, 1977.
Yoshio Ohtsubo.
Optimal control of the service rate and the stopping rule in an
M/G/1 queue.
Mem. Fac. Sci., Kochi Univ., Ser. A 3, 75-103, 1982.
Sigurdur Ólafsson and Leyuan Shi.
Ordinal comparison via the nested partitions method.
Discrete Event Dyn. Syst., 12(2):211-239, 2002.
Niels J. Olieman and Bram van Putten.
Estimation method of multivariate exponential probabilities based on
a simplex coordinates transform.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
G. C. Oliveira, M. V. F. Pereira, S. H. F. Cunha, and S. Granville.
Multi-area capacity expansion model with reliability constraints.
PSCC '87, 1987.
Paul Olsen.
Polyhedral convex feasible regions in stochastic programming with
recourse.
In Proceedings of the IEEE Conference on Decision and Control
including the 14th Symposium on Adaptive Processes (Houston, Tex., 1975),
pages 593-597. Inst. Electr. Electron. Engrs., New York, 1975.
Paul Olsen.
Discretizations of multistage stochastic programming problems.
Math. Programming Stud., 6:111-124, 1976.
Stochastic systems: modeling, identification and optimization, II
(Proc. Sympos., Univ. Kentucky, Lexington, Ky.,1975).
Paul Olsen.
Multistage stochastic programming with recourse as mathematical
programming in an l\sbp space.
SIAM J. Control Optimization, 14(3):528-537, 1976.
Paul Olsen.
Multistage stochastic programming with recourse: the equivalent
deterministic problem.
SIAM J. Control Optimization, 14(3):495-517, 1976.
Paul Olsen.
When is a multistage stochastic programming problem well-defined?
SIAM J. Control Optimization, 14(3):518-527, 1976.
David L. Olson and Scott R. Swenseth.
A linear approximation for chance-constrained programming.
J. Oper. Res. Soc. 38, 261-267, 1987.
A. Orden.
A study of pivot probabilities in LP tableaus.
In Survey of mathematical programming (Proc. Ninth Internat.
Math. Programming Sympos., Budapest, 1976), Vol. 2, pages 141-154.
North-Holland, Amsterdam, 1979.
S.A. Orlovsky.
On formalization of a general fuzzy mathematical problem.
Fuzzy Sets and Systems, 3(3):311-321, 1980.
P. M. Ortigosa, J. L. Redondo, I. García, and J. J. Fernández.
A population global optimization algorithm to solve the image
alignment problem in electron crystallography.
J. Global Optim., 37(4):527-539, 2007.
Shunji Osaki, D.N.P. Murthy, and R.J. Wilson, editors.
Stochastic models in engineering, technology and management.
Pergamon Press, Exeter, 1995.
Papers from the Australian-Japan Workshop held on the Gold Coast,
July 14-16, 1994, Math. Comput. Modelling 22 (1995), no. 10-12.
M.R. Osborne.
A finite algorithm for the rank regression problem.
In Essays in statistical science, Pap. in Honour of P.A.P.
Moran, J. Appl. Probab., Spec. Vol. 19A, 241-252, 1982.
M.R. Osborne and G.A. Watson.
An analysis of the total approximation problem in separable norms,
and an algorithm for the total l\sb 1 problem.
SIAM J. Sci. Statist. Comput., 6(2):410-424, 1985.
Ju.S. Osipov.
On the theory of differential games in distributed parameter
systems.
Sov. Math., Dokl. 16, 1093-1097 translation from Dokl. Akad.
Nauk SSSR 223, 1314-1317 (1975)., 1975.
John Otto, Marius Paraschivoiu, Serhat Ye silyurt, and Anthony T. Patera.
Bayesian-validated computer-simulation surrogates for optimization
and design: error estimates and applications.
Math. Comput. Simulation, 44(4):347-367, 1997.
IMACS-COST Conference on Computational Fluid Dynamics,
Three-dimensional Complex Flows (Lausanne, 1995).
Hiroyuki Ozaki and Peter A. Streufert.
Dynamic programming for non-additive stochastic objectives.
J. Math. Econom., 25(4):391-442, 1996.
Mufit Ozden and Yu-Chi Ho.
A probabilistic solution-generator for simulation.
European J. Oper. Res., 146(1):35-51, 2003.
Tae Je Pak.
Study about statistical properties of extremum estimators in a method
on global random search.
Su-hak, 1:14-19, 1988.
Udatta S. Palekar, Rajan Batta, Robert M. Bosch, and Sharad Elhence.
Modeling uncertainties in plant layout problems.
Eur. J. Oper. Res. 63, No.2, 347-359, 1992.
F. Pan, W. Charlton, and D.P. Morton.
Interdicting smuggled nuclear material.
In D.L. Woodruff, editor, Network Interdiction and Stochastic
Integer Programming, pages 1-20. Kluwer Academic Publishers, Boston, 2003.
Rufang Pan.
A simple solution for fuzzy linear programming.
Nat. Sci. J. Xiangtan Univ. 1987, No.3, 29-36, 1987.
J.-C. Panayiotopoulos.
The multidimensional assignment model with provision for an uncertain
future.
J. Inform. Optim. Sci., 1(1):94-101, 1980.
G. Panda, D.A. Khan, and U.C. Ray.
A jels stochastic inventory model with random demand.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
Jong-Shi Pang.
A parametric linear complementarity technique for optimal portfolio
selection with a risk-free asset.
Oper. Res. 28, 927-941, 1980.
Christos H. Papadimitriou.
Games against nature.
J. Comput. System Sci., 31(2):288-301, 1985.
Special issue: Twenty-fourth annual symposium on the foundations of
computer science (Tucson, Ariz., 1983).
Nikolaos S. Papageorgiou.
Stochastic nonsmooth analysis and optimization in Banach spaces.
In Infinite programming (Cambridge, 1984), volume 259 of
Lecture Notes in Econom. and Math. Systems, pages 226-242, Berlin, 1985.
Springer.
G.P. Papavassilopoulos.
On the probability of existence of pure equilibria in matrix games.
J. Optim. Theory Appl., 87(2):419-439, 1995.
G.P. Papavassilopoulos.
Addendum: "On the probability of existence of pure equilibria in
matrix games" [J. Optim. Theory Appl. 87 (1995), no. 2,
419-439; MR 96h:90149].
J. Optim. Theory Appl., 91(3):729-730, 1996.
Ju.M. Paramonov.
Bestimmung der festzusetzenden Ressource bei einer kleinen Zahl von
vorhergehenden Beobachtungen.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1975, Nr. 5, 120-123,
1975.
P.M. Pardalos and K.G. Ramakrishnan.
On the expected optimal value of random assignment problems:
experimental results and open questions.
Comput. Optim. Appl., 2(3):261-271, 1993.
E. Parent and F. Lebdi.
Bicriterion operation of a water resources system with
reliability-based tradeoffs.
Appl. Math. Comput. 54, No.2-3, 197-213, 1993.
S.R. Parimi and M.Z. Cohn.
Optimal criteria in probabilistic structural design.
In Optim. struct. Des., IUTAM-Symp. Warsaw 1973, 278-293,
1975.
Mahmut Parlar.
Stochastic control of a pension fund model with first-order
Markov-dependent parameters.
Optimal Control Appl. Methods, 2(2):175-189, 1981.
Mahmut Parlar.
A stochastic production planning model with a dynamic chance
constraint.
Eur. J. Oper. Res. 20, 255-260, 1985.
Panos Parpas and Berç Rustem.
Computational assessment of nested benders and augmented Lagrangian
decomposition for mean-variance multistage stochastic problems.
INFORMS J. Comput., 19(2):239-247, 2007.
Panos Parpas, Berç Rustem, and Efstratios N. Pistikopoulos.
Linearly constrained global optimization and stochastic differential
equations.
J. Global Optim., 36(2):191-217, 2006.
Robert Parviainen.
Random assignment with integer costs.
Combin. Probab. Comput., 13(1):103-113, 2004.
S.V. Pashko.
On the convergence rate of the stochastic linearization method.
Kibernetika 1984, No.4, 118-119, 1984.
S.V. Pashko.
The rate of convergence of the stochastic linearization method.
Issled. Operatsii i ASU, 23:9-13, 138, 1984.
S.V. Pashko.
Optimality of the subgradient method for solving problems of
stochastic programming.
Dokl. Akad. Nauk Ukrain. SSR Ser. A, 8:76-79, 88, 1986.
S.V. Pashko.
The complexity of strongly convex problems of stochastic programming.
Kibernetika (Kiev), 2:118-121, 135, 1987.
E. L. Pasiliao.
Transient stochastic models for search patterns.
In Stochastic optimization: algorithms and applications
(Gainesville, FL, 2000), volume 54 of Appl. Optim., pages 265-277.
Kluwer Acad. Publ., Dordrecht, 2001.
Jesús T. Pastor, José L. Ruiz, and Inmaculada Sirvent.
A statistical test for nested radial DEA models.
Oper. Res., 50(4):728-735, 2002.
Nitin R. Patel.
On wait-and-see stochastic linear programmes: an application and an
algorithm.
J. Oper. Res. Soc., 31(8):733-741, 1980.
Vivek Patkar, P.C. Saxena, and Om Parkash.
Dual program for a convex fractional function.
Econom. Comput. Econom. Cybernet. Stud. Res., 15(1):77-80,
1981.
Michael Patriksson and Laura Wynter.
Stochastic mathematical programs with equilibrium constraints.
Oper. Res. Lett., 25(4):159-167, 1999.
Michèle Pavon and Roger J.-B. Wets.
The duality between estimation and control from a variational
viewpoint: the discrete time case.
Math. Programming Stud., 18:1-11, 1982.
Algorithms and theory in filtering and control (Lexington, Ky.,
1980).
Mieczyslaw Pazdur and Anna Pazdur.
Application of the nonlinear least squares method to the estimation
of the parameters of a function for approximation of the results of
measurements.
Zeszyty Nauk. Politech. \'Slk ask. Mat.-Fiz., 32:101-109,
1980.
Martin Pelikan, David E. Goldberg, and Fernando G. Lobo.
A survey of optimization by building and using probabilistic models.
Comput. Optim. Appl., 21(1):5-20, 2002.
Martin Pelikan, Kumara Sastry, and David E. Goldberg.
Scalablity of the Bayesian optimization algorithm.
Internat. J. Approx. Reason., 31(3):221-258, 2002.
Synergies between evolutionary computation and probabilistic
graphical models.
Rainer Pelizaeus.
Konvexitaetsaussagen beim stochastischen linearen Programmieren mit
Wahrscheinlichkeitsrestriktionen.
Oper. Res. Verfahren 30, 130-153, 1979.
Rainer Pelizaeus.
Der Rand des zulaessigen Bereichs beim stochastischen linearen
Optimieren mit Wahrscheinlichkeitsrestriktionen.
Methods Oper. Res. 47, 85-94, 1983.
Rainer Pelizaeus.
Konvexitaetsaussagen beim stochastischen linearen Programmieren.
Zwei Beispiele zur Berechnung alpha-extremaler Punkte.
Methods Oper. Res. 47, 95-106, 1983.
Rainer Pelizaeus.
Korrespondenzen und Wahrscheinlichkeitsrestriktionen beim
stochastischen linearen Optimieren.
Methods Oper. Res. 47, 75-84, 1983.
Jian-ping Peng and Ding-hua Shi.
Improvement of pure random search in global optimization.
J. Shanghai Univ., 4(2):92-95, 2000.
Jin Peng and Baoding Liu.
A framework of birandom theory and optimization methods.
Information, 9(4):629-640, 2006.
Teemu Pennanen.
Epi-convergent discretizations of multistage stochastic programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Teemu Pennanen.
Epi-convergent discretizations of multistage stochastic programs via
integration quadratures.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Teemu Pennanen.
Epi-convergent discretizations of multistage stochastic programs.
Math. Oper. Res., 30(1):245-256, 2005.
Teemu Pennanen and Markku Kallio.
A splitting method for stochastic programs.
Ann. Oper. Res., 142:259-268, 2006.
Teemu Pennanen and Alan J. King.
Arbitrage pricing of american contingent claims in incomplete markets
- a convex optimization approach.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Teemu Pennanen and Matti Koivu.
Integration quadratures in discretization of stochastic programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2002.
Teemu Pennanen and Matti Koivu.
Epi-convergent discretizations of stochastic programs via integration
quadratures.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Teemu Pennanen and Matti Koivu.
Epi-convergent discretizations of stochastic programs via integration
quadratures.
Numer. Math., 100(1):141-163, 2005.
Allon G. Percus and Olivier C. Martin.
The stochastic traveling salesman problem: finite size scaling and
the cavity prediction.
J. Statist. Phys., 94(5-6):739-758, 1999.
M. V. F. Pereira, N. M. Campodónico, B. G. Gorenstin, and J. P. Costa.
Application of stochastic optimization to power system planning and
operation.
In Proceedings of the IEEE Stockholm Power Tech, pages
234-239, Stockholm, Sweden, 1995.
M. V. F. Pereira and L. M. V. G Pinto.
Stochastic optimization of a multireservoir hydroelectric system-a
decomposition approach.
Water Resources Research, 21(6):779-792, 1985.
M.V.F. Pereira and L.M.V.G. Pinto.
Multi-stage stochastic optimization applied to energy planning.
Math. Programming, 52(2, Ser. B):359-375, 1991.
I.I. Perel'man.
Stationary adaptive search strategy as an alternative to the method
of stochastic approximation.
Autom. Remote Control 48, No.11, 1517-1527 translation from
Avtom. Telemekh. 1987, No.11, 132-143 (1987)., 1987.
A.G. Perevozchikov.
A method for the approximation of the pseudogradient mapping of the
function of the connected maximum.
Zh. Vychisl. Mat. i Mat. Fiz., 31(3):353-362, 1991.
A.G. Perevozchikov.
On the approximation of generalized stochastic gradients of random
regular functions.
Zh. Vychisl. Mat. i Mat. Fiz., 31(5):681-688, 1991.
A.G. Perevozchikov.
The stochastic method of generalized Clarke gradients for solving
two- stage problems of stochastic programming with coupled variables.
Comput. Math. Math. Phys. 33, No.3, 411-414 translation from Zh.
Vychisl. Mat. Mat. Fiz. 33, No.3, 453-455 (1993)., 1993.
G. Pérez-Lechuga, M. M. Álvarez-Suárez, J. Garnica-González,
H. Niccolas-Morales, and F. Venegas-Martínez.
Stochastic linear programming to optimize some stochastic systems.
WSEAS Trans. Syst., 5(9):2263-2268, 2006.
C. Perkgoz, K. Kato, H. Katagiri, and M. Sakawa.
An interactive fuzzy satisficing method for multiobjective stochastic
integer programming problems through variance minimization model.
Sci. Math. Jpn., 60(2):327-336, 2004.
Cary D. Perttunen.
A nonparametric global optimization method using the rank
transformation.
In Proceedings of the 28th IEEE Conference on Decision and
Control, Vol. 1-3 (Tampa, FL, 1989), pages 888-893, New York, 1989. IEEE.
Cary D. Perttunen and Bruce E. Stuckman.
The normal score transformation applied to a multi-univariate method
of global optimization.
J. Global Optim., 2(2):167-176, 1992.
Anatoli A. Pervozvanski and Vladimir G. Gajcgori.
Operative control of industrial activity as a problem of stochastic
programming.
Systems Sci., 3(1):91-95, 1977.
A.A. Pervozvanskij and V.G. Gajtsgori.
Decomposition, aggregation and approximate optimization.
(Dekompozitsiya, agregirovanie i priblizhennaya optimizatsiya).
Teoriya i Metody Sistemnogo Analiza. Moskva: "Nauka"., 1979.
Robert J. Peters, Klaas Boskma, and Hendrik A.E. Kupper.
Stochastic programming in production planning: A case with
none-simple recourse.
Statistica Neerlandica 31, 113-126, 1977.
Robert J. Peters, Kai-Ching Chu, and Mohammad Jamshidi.
Optimal operation of a water resources system by stochastic
programming.
In Math. Progr. in Use, Math. Progr. Study 9, 152-175, 1978.
I. Petersen.
Reduction of risk using a differentiated approach.
In Stochastic optimization, Proc. Int. Conf., Kiev/USSR 1984,
Lect. Notes Control Inf. Sci. 81, 496-500, 1986.
L. Petersen.
Stochastic optimization by smoothing.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1969, No.2, 36-44, 1969.
Dobiás Petr.
Contamination technigue for two-stage stochastic integer programs.
In Proceedings of 18th International Conference; Mathematical
Methods in Economics, pages 33-38, 2000.
V.V. Petrov, V.M. Ageev, A.V. Zaporozec, A.V. Kort'ev, V.M. Kostjukov,
S.B. Medvedev, and I.N. Poljakov.
Information theory of complex systems functioning under conditions of
incomplete information.
In Engineering cybernetics, Vol. 13 (Russian), pages 121-151.
Akad. Nauk SSSR, Vsesoyuz. Inst. Nauchn. i Tekhn. Informatsii, Moscow, 1980.
U. Pferschy.
The random linear bottleneck assignment problem.
RAIRO Rech. Opér., 30(2):127-142, 1996.
G. Ch. Pflug.
Stepsize rules, stopping times and their implementation in stochastic
quasigradient algorithms.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 353-372. Springer, Berlin, 1988.
G. Ch. Pflug, A. \'Swi etanowski, E. Dockner, and H. Moritsch.
The Aurora financial management system: model and parallel
implementation design.
Ann. Oper Res., 99:189-206 (2001), 2000.
Applied mathematical programming and modeling, IV (Limassol, 1998).
G.Ch. Pflug.
On the penalization method in convex stochastic programming.
In Operations research in progress, Theory Decis. Libr. 32,
49-55, 1982.
G.Ch. Pflug and A. Ruszczy\'nski, editors.
Thirteenth EURO Summer Institute: Stochastic Optimization.
North-Holland, Amsterdam, 1997.
EJOR 101, No. 2.
Georg Ch. Pflug.
On the convergence of a penalty-type stochastic optimization
procedure.
J. Inform. Optim. Sci., 2(3):249-258, 1981.
Georg Ch. Pflug.
Stochastic minimization with constant step-size: Asymptotic laws.
SIAM J. Control Optimization 24, 655-666, 1986.
Georg Ch. Pflug.
Derivatives of probability measures - concepts and applications to
the optimization of stochastic systems.
In Discrete event systems: models and applications, IIASA
Conf., Sopron/Hung. 1987, Lect. Notes Control Inf. 103, 252-274, 1988.
Georg Ch. Pflug.
Optimization of stochastic models.
Kluwer Academic Publishers, Boston, MA, 1996.
The interface between simulation and optimization.
Georg Ch. Pflug.
Stochastic programs and statistical data.
Ann. Oper. Res., 85:59-78, 1999.
Stochastic programming. State of the art, 1998 (Vancouver, BC).
Georg Ch. Pflug and Andrzej Ruszczy\'nski.
Risk measures for income streams.
Stochastic Programming E-Print Series, http://www.speps.org,
2001.
Georg Ch. Pflug, Andrzej Ruszczy\'nski, and Rüdiger Schultz.
On the Glivenko-Cantelli problem in stochastic programming:
linear recourse and extensions.
Math. Oper. Res., 23(1):204-220, 1998.
Georg Ch. Pflug, Andrzej Ruszczy\'nski, and Rüdiger Schultz.
On the Glivenko-Cantelli problem in stochastic programming:
mixed-integer linear recourse.
Math. Methods Oper. Res., 47(1):39-49, 1998.
Georg Ch. Pflug and Heinz Weisshaupt.
Probability gradient estimation by set-valued calculus and
applications in network design.
SIAM J. Optim., 15(3):898-914 (electronic), 2005.
Huyên Pham.
On quadratic hedging in continuous time.
Math. Methods Oper. Res., 51(2):315-339, 2000.
V.C. Phan.
Quelques théorèmes de point fixe pour des multifonctions
aléatoires de type de contraction.
Travaux Sém. Anal. Convexe, 10(1):exp. no. 7, 27, 1980.
C. R. Philbrick and P. K. Kitanidis.
Limitations of deterministic optimization applied to reservoir
operations.
Journal of Water Resources Planning and Management-ASCE,
125(3):135-142, 1999.
A.T. Phillips, J.B. Rosen, and M. van Vliet.
A parallel stochastic method for solving linearly constrained concave
global minimization problems.
J. Global Optim., 2(3):243-258, 1992.
Conference on Computational Methods in Global Optimization, II
(Princeton, NJ, 1991).
A. B. Philpott.
Stochastic optimization and yacht racing.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 315-336. SIAM, Philadelphia, PA, 2005.
A. B. Philpott, M. Craddock, and H. Waterer.
Hydro-electric unit commitment subject to uncertain demand.
European Journal of Operational Research, 125(2):410-424,
2000.
Andy Philpott and Geoff Leyland.
Rowing to Barbados.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Nguyen Van Pho.
On a mathematical model of the optimal reliability theory and some
mechanical applications.
Teor. Prilozhna Mekh. 1986, No.2, 100-106, 1986.
Mustafa Ç. Pi nar.
Robust scenario optimization based on downside-risk measure for
multi-period portfolio selection.
OR Spectrum, 29(2):295-309, 2007.
James B. Pickens, John G. Hof, and Brian M. Kent.
Use of chance-constrained programming to account for stochastic
variation in the A-matrix of large-scale linear programs: a forestry
application.
Ann. Oper. Res., 31(1-4):511-526, 1991.
Stochastic programming, Part II (Ann Arbor, MI, 1989).
Nanda Piersma.
A probabilistic analysis of the capacitated facility location
problem.
J. Comb. Optim., 3(1):31-50, 1999.
Francisco A. Pino and Pedro A. Morettin.
The consistency of the L\sb 1-norm estimates in ARMA
models.
Comm. Statist. Theory Methods, 22(8):2185-2206, 1993.
J. Pinter.
Hybrid procedures for solving non-smooth stochastic problems of
constrained minimization.
Vestn. Mosk. Univ., Ser. XV 1982, No.1, 39-49, 1982.
J. Pinter.
Mixed procedures for the solution of nonsmooth stochastic
conditional minimization problems.
Mosc. Univ. Comput. Math. Cybern. 1982, No.1, 47-57, 1982.
J. Pintér.
Stochastic optimization methods for solving mathematical programming
problems.
In Statistics and probability (Visegrád, 1982), pages
271-282. Reidel, Dordrecht, 1984.
J. Pintér.
Deterministic approximations of probability inequalities.
Z. Oper. Res., 33(4):219-239, 1989.
Janos Pinter.
A stochastic programming model, applied to water resources
management.
Budapest: Computung Center for Universities., 1975.
János Pintér.
Evaluation of a stochastic gradient optimization algorithm,
volume 23 of ESZK.
Computing Center for Universities Department of Applied Mathematics,
Budapest, 1978.
With Hungarian and Russian summaries.
János Pintér.
On the convergence and numerical effectiveness of random search
procedures.
Alkalmaz. Mat. Lapok, 4(3-4):197-228 (1979), 1978.
Janos Pinter.
On a stochastic model of reservoir system sizing.
In Optimization techniques, Proc. 9th IFIP Conf., Warsaw 1979,
Part 2, Lect. Notes Control Inf. Sci. 23, 546-558, 1980.
János Pintér.
Hybrid optimization procedures for the solution of nonsmooth
stochastic problems.
Alkalmaz. Mat. Lapok, 7(1-2):83-97, 1981.
János Pintér.
Stochastic procedures for solving optimization problems.
Alkalmaz. Mat. Lapok, 7(3-4):217-252, 1981.
Janos Pinter.
Stochastically combined optimization procedures, their convergence
and numerical performance.
Methods Oper. Res. 43, 143-150, 1981.
János Pintér.
Convergence properties of stochastic optimization procedures.
Math. Operationsforsch. Statist. Ser. Optim., 15(3):405-427,
1984.
János Pintér.
Contributions to the methodology of stochastic optimization.
In Stochastic programming (Gargnano, 1983), volume 76 of
Lecture Notes in Control and Inform. Sci., pages 247-257. Springer, Berlin,
1986.
Janos Pinter.
Stochastic modelling and optimization for environmental management.
Ann. Oper. Res. 31, 527-544, 1991.
J.D. Pinter.
On the maximal distance between two series of empirical distribution
functions, with application to an inventory problem.
Methods of Operations Research, 29:623-636, 1978.
J.D. Pinter.
A modified Bernstein-technique for estimating noise-perturbed
function values.
Calcolo, 22:241-247, 1985.
J.D. Pinter.
Stochastic decision models for RA&M (risk analysis and
management): A brief methodological overview.
Research Report 90.068, National Institute for Inland Water
Management and Waste Water Treatment, Lelystad, 1990.
J.D. Pinter.
Global Optimization in Action.
Kluwer Academic Publishers, Dordrecht / Boston / London, 1996.
J.D. Pinter.
A model development system for global optimization.
In R. De Leone, A. Murli, P.M. Pardalos, and G. Toraldo, editors,
High Performance Software for Nonlinear Optimizaton: Status and
Perspectives, pages 301-314. Kluwer Academic Publishers, Dordrecht / Boston
/ London, 1998.
J.D. Pinter.
LGO - A Model Development System for Continuous Global
Optimization. User's Guide.
Pinter Consulting Services, Inc., Halifax, NS, Canada, 1999.
J.D. Pinter and L. Somlyody.
A stochastic lake eutrophication management model.
In V.I. Arkin, A.N. Shiriaev, and R. Wets, editors, Stochastic
Optimization (Kiev, Sept. 1984). Lecture Notes in Control and Information
Sciences 81, pages 501-512, Berlin Heidelberg New York, 1986.
Springer-Verlag.
J.D. Pinter and D.T. van der Molen.
Environmental model calibration under different problem
specifications: An application to the model SED.
Ecological Modelling, 68:1-19, 1993.
Zdenek Piras.
Use of stochastic programming for the design of elastic-plastic one
dimensional systems.
Acta techn. CSAV 17, 667-694, 1972.
F.M. Pires and J.J. Júdice.
Direct methods for convex quadratic problems with restrictions only
on the values of the variables.
In Proceedings of the XIIth Portuguese-Spanish Conference on
Mathematics, Vol. III (Portuguese) (Braga, 1987), pages 289-296, Braga,
1987. Univ. Minho.
O. Pirohanic.
Suboptimal stochastic control strategy and an alternative cost
decomposition.
Int. J. Control 44, 1297-1318, 1986.
A.B. Piunovskii.
Control of impulsive processes in problems with constraints.
Avtomat. i Telemekh., 4:75-89, 1994.
Luis M. Pla, Josep Conde, and Jesús Pomar.
Stochastic dynamic programming the sow replacement problem.
In Proceedings of the 3rd Catalan Days on Applied Mathematics
(Lleida, 1996), pages 175-184. Univ. Lleida, Lleida, 199?
V. P. Plagianakos, G. D. Magoulas, and M. N. Vrahatis.
Learning rate adaptation in stochastic gradient descent.
In Advances in convex analysis and global optimization
(Pythagorion, 2000), volume 54 of Nonconvex Optim. Appl., pages
433-444. Kluwer Acad. Publ., Dordrecht, 2001.
E.L. Plambeck, B.-R. Fu, S.M. Robinson, and R. Suri.
Throughput optimization in tandem production lines via nonsmooth
programming.
In J. Schoen, editor, Proceedings of 1993 Summer Computer
Simulation Conference, pages 70-75, San Diego, CA, 1993. Society for
Computer Simulation.
Erica L. Plambeck, Bor-Ruey Fu, Stephen M. Robinson, and Rajan Suri.
Sample-path optimization of convex stochastic performance functions.
Math. Programming, 75(2, Ser. B):137-176, 1996.
Approximation and computation in stochastic programming.
Stanley R. Pliska.
Duality theory for some stochastic control models.
In Stochastic differential systems, Proc. 2nd Conf., Bad Honnef
1982, Lect. Notes Control Inf. Sci. 43, 329-337, 1982.
Ernst Ploechinger.
Limit theorems on the Robbins-Monro process for different variance
behaviors of the stochastic gradient.
In Stochastic optimization. Numerical methods and technical
applications, Proc. GAMM/IFIP-Workshop, Neubiberg/Ger. 1990, Lect. Notes
Econ. Math. Syst. 379, 27-80, 1992.
Ernst Ploechinger.
Realisierung von adaptiven Schrittweiten fuer stochastische
Approximationsverfahren bei unterschiedlichem Varianzverhalten des
Schaetzfehlers. (Realization of adaptive step widths for stochastic
approximation methods at different behaviour of the variance of the
estimation processor)., 1992.
Frederick van der Ploeg.
A closed-form solution for a model of precautionary saving.
Rev. Econ. Stud. 60, No.2, 385-395, 1993.
V.V. Podinovskii.
Lexikographische Optimierungsprobleme unter
Unbestimmtheitsbedingungen.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1973, Nr. 1, 32-37, 1972.
V.V. Podinovskii.
Ueber die Loesung multikriterialer Probleme als monokriteriale
Optimierungsprobleme unter Unbestimmtheit.
Avtomatika vycislit. Tehn., Riga 1976, Nr. 2, 45-49, 1976.
V.V. Podinovskiy.
Lexicographical problems of optimization under conditions of
indeterminacy.
Engrg. Cybernetics, 11(1):28-33, 1973.
J. Poepplau.
Die Anwendung einer (mu/rho,lambda)-Evolutionsstrategie zur direkten
Minimierung eines nichtlinearen Funktionals unter Verwendung von FE-
Ansatzfunktionen am Beispiel des Brachistochronenprolbems.
Z. Angew. Math. Mech. 61, T 305-T 307, 1981.
V. Pogozel'ski.
An application of a certain classification procedure to the
improvement of a search for the global minimum.
In Problems of random search, 2 (Russian), pages 127-130, 221.
Izdat. "Zinatne", Riga, 1973.
V. Pogozel'ski.
Determination of the best sequence for testing the fulfillment of
constraints when using a statistical algorithm.
In Problems of random search, 2 (Russian), pages 151-156, 221.
Izdat. "Zinatne", Riga, 1973.
M. Pogu and J.E. Souza de Cursi.
Global optimization by random perturbation of the gradient method
with a fixed parameter.
J. Global Optim., 5(2):159-180, 1994.
Elijah Polak, Roger J.-B. Wets, and Armen der Kiureghian.
On an approach to optimization problems with a probabilistic cost and
or constraints.
In Nonlinear optimization and related topics (Erice, 1998),
pages 299-315. Kluwer Acad. Publ., Dordrecht, 2000.
B.T. Poljak.
Nonlinear programming methods in the presence of noise.
In Survey of mathematical programming (Proc. Ninth Internat.
Math. Programming Sympos., Budapest, 1976), Vol. 2, pages 155-165,
Amsterdam, 1979. North-Holland.
B.T. Poljak and Ya. Z. Tsypkin.
Optimal and robust methods for unconditional optimization.
In Control science and technology for the progress of society,
Vol. 1 (Kyoto, 1981), pages 519-523. IFAC, Laxenburg, 1982.
M.A. Pollatschek.
Bounds for stochastic convex programs.
Z. Operations Res. Ser. A-B, 18:A27-A39, 1974.
B. T. Polyak.
Robust stability of interval matrices: a stochastic approach.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 202-207. Springer, Berlin, 1998.
B. T. Polyak.
Random algorithms for solving convex inequalities.
In Inherently parallel algorithms in feasibility and
optimization and their applications (Haifa, 2000), volume 8 of Stud.
Comput. Math., pages 409-422. North-Holland, Amsterdam, 2001.
B. T. Polyak and Ya. Z. Tsypkin.
Optimal and robust methods for stochastic optimization.
Nova J. Math. Game Theory Algebra, 6(2-3):163-176, 1997.
B.T. Polyak.
Methods for solving constrained extremum problems in the presence of
random noise.
U.S.S.R. Comput. Math. Math. Phys. 19, No.1, 72-81 translation
from Zh. Vychisl. Mat. Mat. Fiz. 19, 70-78 (1979)., 1980.
B.T. Polyak.
A new method of stochastic approximation type.
Avtomat. i Telemekh., 7:98-107, 1990.
B.T. Polyak and A.B. Tsybakov.
Optimal order of accuracy of search algorithms in stochastic
optimization.
Probl. Inf. Transm. 26, No.2, 126-133 translation from Probl.
Peredachi Inf. 26, No.2, 45-53 (1990)., 1990.
B.T. Polyak and Ya. Z. Tsypkin.
Optimal pseudogradient adaptation algorithms.
Automat. Remote Control, 41(8, part 1):1101-1110 (1981), 1980.
B.T. Polyak and Ya. Z. Tsypkin.
Optimal algorithms for criterial optimization under conditions of
uncertainty.
Dokl. Akad. Nauk SSSR, 273(2):315-318, 1983.
B.T. Polyak and Ya.Z. Tsypkin.
Optimal pseudogradient stochastic-optimization algorithms.
Sov. Phys., Dokl. 25, 85-87 translation from Dokl. Akad. Nauk
SSSR 250, 1084-1087 (1980)., 1980.
B.T. Polyak and Ya.Z. Tsypkin.
Criterion algorithms of stochastic optimization.
Autom. Remote Control 45, 766-774 translation from Avtom.
Telemekh. 1984, No.6, 95-104 (1984)., 1984.
George H. Polychronopoulos and John N. Tsitsiklis.
Stochastic shortest path problems with recourse.
Networks, 27(2):133-143, 1996.
Andreas Ponn.
Optimale Zentrierung von Produktionsprozessen. (Optimal centering of
production processes)., 1989.
Chandra A. Poojari, Cormac Lucas, and Gautam Mitra.
Robust solution and risk measures for a supply chain planning problem
under uncertainty.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
Chandra A. Poojari and Boby Varghese.
Genetic algorithm based technique for solving chance constrained
problems.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
Pavel Popela.
Stochastic programming models and methods for technical applications.
In Summer School DATASTAT 01, Proceedings (Cihák Cottage),
volume 11 of Folia Fac. Sci. Natur. Univ. Masaryk. Brun. Math., pages
181-206, Brno, 2002. Masaryk Univ.
E. Popova and D. Morton.
Adaptive stochastic manpower scheduling.
In Proceedings of the Winter Simulation Conference, pages
661-668, 1998.
I. Popova, D. P. Morton, E. Popova, and J. Yau.
Optimizing benchmark-based portfolios with hedge funds.
The Journal of Alternative Investments, 10:35-55, 2007.
S.M. Porockii.
Stochastische Zweietappenprobleme vom Transporttyp.
Izv. Akad. Nauk SSSR, Tekh. Kibernet. 1977, No.2, 23-31, 1977.
Z. Porosi\'nski and K. Szajowski.
Random priority two-person full-information best choice problem with
imperfect observation.
Appl. Math. (Warsaw), 27(3):251-263, 2000.
S.M. Porotskiy.
Two-step stochastic problems of the transport type.
Engrg. Cybernet., 15(2):20-29 (1978), 1977.
Evan L. Porteus.
An adjustment to the Norman-White approach to approximating dynamic
programs.
Oper. Res. 27, 1203-1207, 1979.
Dominique Potier.
Algorithmes de coordination. Application a la gestion d'unites de
production interdependantes.
IRIA, Cahier 11, 241-387, 1972.
B. Pourbabai.
A heuristic algorithm for a stochastic transportation network.
Optimization, 25(1):91-95, 1992.
B. Pourbabai.
An optimal policy for controlling the flow time in an assembly line
system.
Optimization, 30(2):177-189, 1994.
Behnam Pourbabai.
A class of chance constrained network optimization problems.
Appl. Math. Lett. 3, No.3, 91-94, 1990.
Behnam Pourbabai.
Minimum required local storages of an automated assembly system.
J. Oper. Res. Soc. 43, No.2, 95-109, 1992.
Behnam Pourbabai.
An operational strategy for throughput maximization and bottleneck
control in an assembly line system: By selection of the processing rates.
J. Oper. Res. Soc. 44, No.10, 1003-1011, 1993.
Warren Powell, Andrzej Ruszczynski, and Huseyin Topaloglu.
Learning algorithms for separable approximations of stochastic
optimization problems.
Stochastic Programming E-Print Series, http://www.speps.org,
2002.
Warren Powell, Andrzej Ruszczy\'nski, and Huseyin Topaloglu.
Learning algorithms for separable approximations of discrete
stochastic optimization problems.
Math. Oper. Res., 29(4):814-836, 2004.
Warren B. Powell.
A comparative review of alternative algorithms for the dynamic
vehicle allocation problem.
In Vehicle routing: Methods and studies, Stud. Manage. Sci.
Syst. 16, 249-291 , 1988.
Warren B. Powell and Raymond K. Cheung.
Stochastic programs over trees with random arc capacities.
Networks, 24(3):161-175, 1994.
Warren B. Powell and Huseyin Topaloglu.
Fleet management.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 185-215. SIAM, Philadelphia, PA, 2005.
A.S. Poznjak.
Use of learning automata for the control of random search.
Autom. Remote Control 33, 1992-2000 translation from Avtom.
Telemekh. 1972, No.12, 88-97 (1972)., 1972.
A.S. Poznjak.
Learning automata in stochastic programming problems.
Autom. Remote Control 34, 1608-1619 translation from Avtom.
Telemekh. 1973, No.10, 84-96 (1973)., 1973.
A. S. Poznyak and K. Najim.
Learning automata and stochastic optimization.
Springer-Verlag London Ltd., London, 1997.
A.S. Poznyak.
Recursive stochastic gradient procedures in the presence of dependent
noise.
In Stochastic optimization (Kiev, 1984), volume 81 of
Lecture Notes in Control and Inform. Sci., pages 522-533. Springer, Berlin,
1986.
A.S. Poznyak and Ya.Z. Tsypkin.
Class-wise optimal algorithms for optimization in correlated-noise
conditions.
U.S.S.R. Comput. Math. Math. Phys. 24, 112-122, 1984.
A.S. Poznyak and Ya.Z. Tsypkin.
Optimization algorithms optimal on a class involving correlated
noise.
Zh. Vychisl. Mat. Mat. Fiz. 24, No.6, 806-822, 1984.
L. Praly.
Towards a direct adaptive control scheme for general disturbed MIMO
systems.
In Analysis and optimization of systems, Proc. 5th int. Conf.,
Versailles 1982, Lect. Notes Control Inf. Sci. 44, 353-366, 1982.
A. Prékopa.
On probabilistic constrained programming.
In Proceedings of the Princeton Symposium on Mathematical
Programming (Princeton Univ., 1967), pages 113-138, Princeton, N.J., 1970.
Princeton Univ. Press.
A. Prékopa.
On the number of vertices of random convex polyhedra.
Period. Math. Hungar., 2:259-282, 1972.
Collection of articles dedicated to the memory of Alfréd Rényi,
I.
A. Prekopa.
Ein stochastisches dynamisches Programmierungsmodell.
In 18. internat. wiss. Kolloqu., Ilmenau 1 , 49-50, 1973.
A. Prékopa.
Optimal control of a storage level using stochastic programming.
Problems of Control and Information Theory/Problemy Upravlenija
i Teorii Informacii, 4(3):193-204, 1975.
A. Prékopa.
Dynamic type stochastic programming models.
In Studies on mathematical programming (Papers, Third Conf.
Math. Programming, Mátrafüred, 1975), volume 1 of Math. Methods
Oper. Res., pages 127-145, Budapest, 1980. Akad. Kiadó.
A. Prekopa.
Optimization under probabilistic constraints and its applications in
statistics.
In Computational statistics, Proc. 6th Symp., Prague 1984,
446-457, 1984.
A. Prékopa.
Numerical solution of probabilistic constrained programming problems.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 123-139. Springer, Berlin, 1988.
A. Prékopa.
Dual method for the solution of a one-stage stochastic programming
problem with random RHS obeying a discrete probability distribution.
Z. Oper. Res., 34(6):441-461, 1990.
A. Prékopa and P. Kelle.
Reliability type inventory models based on stochastic programming.
In Survey of mathematical programming (Proc. Ninth Internat.
Math. Programming Sympos., Budapest, 1976), Vol. 2, pages 167-182.
North-Holland, Amsterdam, 1979.
A. Prekopa and A. Ruszczynski, editors.
Special Issue on Stochastic Programming.
Taylor & Francis Group, London, etc., 2002.
Optimization Methods and Software (OMS), Volume 17, Number 3.
A. Prékopa and T. Szántai.
On multi-stage stochastic programming (with application to optimal
control of water supply).
In Progress in operations research, Vols. I, II (Proc. Sixth
Hungarian Conf., Eger, 1974), pages 733-755. Colloq. Math. Soc. János
Bolyai, Vol. 12, Amsterdam, 1976. North-Holland.
A. Prékopa and T. Szántai.
Flood control reservoir system design using stochastic programming.
Math. Programming Stud., 9:138-151, 1978.
Mathematical programming in use.
A. Prekopa and T. Szantai.
On optimal regulation of a storage level with application to the
water level regulation of a lake.
In Survey of mathematical programming, Proc. int. Symp., Vol.
2, Budapest 1976, 183-210, 1980.
A. Prékopa and T. Szántai.
Flood control reservoir system design using stochastic programming.
Tanulmányok-MTA Számitástech. Automat. Kutató Int.
Budapest, 167:155-177, 1985.
Studies in applied stochastic programming, I.
A. Prékopa and R.J.-B. Wets, editors.
Stochastic programming 84. I, Amsterdam, 1986. North-Holland
Publishing Co.
Math. Programming Stud. No. 27 (1986).
A. Prékopa and R.J.-B. Wets, editors.
Stochastic programming 84. II, Amsterdam, 1986.
North-Holland Publishing Co.
Math. Programming Stud. No. 28 (1986).
András Prékopa.
Logarithmic concave measures with application to stochastic
programming.
Acta Sci. Math. (Szeged), 32:301-316, 1971.
András Prékopa.
A class of stochastic programming decision problems.
Math. Operationsforsch. Statist., 3(5):349-354, 1972.
András Prékopa.
Laws of large numbers for random linear programs.
Math. Systems Theory, 6:277-288, 1972.
András Prékopa.
Contributions to the theory of stochastic programming.
Math. Programming, 4:202-221, 1973.
Andras Prekopa.
On logarithmic concave measures and functions.
Acta Sci. math. 34, 335-343, 1973.
Andras Prekopa.
Programming under probabilistic constraints with a random technology
matrix.
Math. Operationsforsch. Statistik 5, 109-116, 1974.
Andras Prekopa.
New proof for the basic theorem of log concave measures.
Alkalmazott Mat. Lapok 1, 385-389, 1977.
András Prékopa, editor.
Studies in applied stochastic programming. I.
Magyar Tudományos Akadémia, Budapest, 1978.
Tanulmányok-MTA Számitástech. Automat. Kutató Int. Budapest
No. 80 (1978).
András Prékopa.
Logarithmic concave measures and related topics.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 63-82. Academic Press, London, 1980.
András Prékopa.
Network planning using two-stage programming under uncertainty.
In Recent results in stochastic programming (Proc. Meeting,
Oberwolfach, 1979), volume 179 of Lecture Notes in Econom. and Math.
Systems, pages 215-237. Springer, Berlin, 1980.
András Prékopa.
Stochastic programming models and their application.
Tanulmányok-MTA Számitástech. Automat. Kutató Int.
Budapest, 106:25, 1980.
András Prékopa.
The use of stochastic programming for the solution of some problems
in statistics and probability.
In Extremal methods and systems analysis (Internat. Sympos.,
Univ. Texas, Austin, Tex., 1977), volume 174 of Lecture Notes in
Econom. and Math. Systems, pages 522-538, Berlin, 1980. Springer.
András Prékopa.
Dynamic type stochastic programming models.
Tanulmányok-MTA Számitástech. Automat. Kutató Int.
Budapest, 167:179-209, 1985.
Studies in applied stochastic programming, I.
András Prékopa, editor.
Studies in applied stochastic programming. I.
MTA Számitástechn. Automat. Kutató Intézet, Budapest, 1985.
Tanulmányok-MTA Számitástech. Automat. Kutató Int. Budapest
No. 167 (1985).
András Prékopa.
Boole-Bonferroni inequalities and linear programming.
Oper. Res., 36(1):145-162, 1988.
András Prékopa.
Stochastic programming, volume 324 of Mathematics and its
Applications.
Kluwer Academic Publishers Group, Dordrecht, 1995.
András Prékopa.
The use of discrete moment bounds in probabilistic constrained
stochastic programming models.
Ann. Oper. Res., 85:21-38, 1999.
Stochastic programming. State of the art, 1998 (Vancouver, BC).
András Prékopa.
Discrete higher order convex functions and their applications.
In Generalized convexity and generalized monotonicity
(Karlovassi, 1999), pages 21-47. Springer, Berlin, 2001.
András Prékopa.
On the concavity of multivariate probability distribution functions.
Oper. Res. Lett., 29(1):1-4, 2001.
Andras Prekopa, Istvan Deak, Sandor Ganczer, and Karoly Patyi.
The STABIL stochastic programming model and its experimental
application to the electrical energy sector of the Hungarian economy.
In Stochastic programming, Proc. int. Conf., Oxford 1974,
369-385, 1980.
András Prékopa, István Deák, Sándor Ganczer, and Károly
Patyi.
The STABIL stochastic programming model and its
experimental application to the electrical energy sector of the Hungarian
economy.
Tanulmányok-MTA Számitástech. Automat. Kutató Int.
Budapest, 167:5-30, 1985.
Studies in applied stochastic programming, I.
András Prékopa, Sándor Ganczer, István Deák, and Károly
Patyi.
The stochastic programming model STABIL, and its
experimental application to the Hungarian electrical power industry.
Alkalmaz. Mat. Lapok, 1(1-2):3-22, 1975.
András Prékopa and Xiaoling Hou.
A stochastic programming model to find optimal sample sizes to
estimate unknown parameters in an LP.
Oper. Res. Lett., 32(1):59-67, 2004.
András Prékopa and Péter Kelle.
Inventory models of reliability type based on stochastic programming.
Alkamaz. Mat. Lapok, 2(1-2):1-16, 1976.
András Prékopa and Wenzhong Li.
Solution of and bounding in a linearly constrained optimization
problem with convex, polyhedral objective function.
Math. Programming, 70(1, Ser. A):1-16, 1995.
András Prékopa, Jianmin Long, and Tamás Szántai.
New bounds and approximations for the probability distribution of the
length of the critical path.
In Dynamic stochastic optimization (Laxenburg, 2002), volume
532 of Lecture Notes in Econom. and Math. Systems, pages 293-320.
Springer, Berlin, 2004.
András Prékopa, Tamás Rapcsák, and István Zsuffa.
A new method for serially linked reservoir system design using
stochastic programming.
Tanulmányok-MTA Számitástech. Automat. Kutató Int.
Budapest, 167:75-97, 1985.
Studies in applied stochastic programming, I.
András Prékopa and Andrzej Ruszczy\'nski, editors.
Stochastic programming.
Taylor & Francis Ltd., Reading, 2002.
Optim. Methods Softw. 17 (2002), no. 3.
András Prékopa and Tamás Szántai.
A new multivariate gamma distribution and its fitting to empirical
streamflow data.
Tanulmányok-MTA Számitástech. Automat. Kutató Int.
Budapest, 167:99-118, 1985.
Studies in applied stochastic programming, I.
András Prékopa, Béla Vizvári, and Tamás Badics.
Programming under probabilistic constraint with discrete random
variable.
In New trends in mathematical programming, pages 235-255.
Kluwer Acad. Publ., Boston, MA, 1998.
Daniele Pretolani.
A directed hypergraph model for random time dependent shortest paths.
European J. Oper. Res., 123(2):315-324, 2000.
Advances in theory and practice of combinatorial optimization (Puerto
de la Cruz, 1997).
Geoffrey Pritchard and Golbon Zakeri.
Upper bounds for Gaussian stochastic programs.
Math. Program., 86(1, Ser. A):51-63, 1999.
Fabio Privileggi.
A characterization for solutions of stochastic discrete time
optimization models.
Riv. Mat. Sci. Econom. Social., 18(2):165-180, 1995.
Fukakujissei o fukumu shisutemu ni okeru saitekika (Optimization methods
for mathematical systems with uncertainty), Kyoto, 1997. Kyoto University
Research Institute for Mathematical Sciences.
S¯urikaisekikenky¯usho K¯oky¯uroku No. 978 (1997).
L. Pronzato, H. P. Wynn, and A. A. Zhigljavsky.
Finite sample behaviour of an ergodically fast line-search algorithm.
Comput. Optim. Appl., 14(1):75-86, 1999.
L. Pronzato, H.P. Wynn, and A.A. Zhigljavsky.
Stochastic analysis of convergence via dynamic representation for a
class of line-search algorithms.
Combin. Probab. Comput., 6(2):205-229, 1997.
Luc Pronzato.
Adaptive optimization and D-optimum experimental design.
Ann. Statist., 28(6):1743-1761, 2000.
Luc Pronzato and Eric Walter.
Experimental design for estimating the optimum point in a response
surface.
Acta Appl. Math., 33(1):45-68, 1993.
Stochastic optimization.
Luc Pronzato, Henry P. Wynn, and Anatoly A. Zhigljavsky.
Dynamical search.
Chapman & Hall/CRC, Boca Raton, FL, 2000.
Applications of dynamical systems in search and optimization,
Interdisciplinary statistics.
Luc Pronzato, Henry P. Wynn, and Anatoly A. Zhigljavsky.
Dynamical search.
Chapman & Hall/CRC, Boca Raton, FL, 2000.
Applications of dynamical systems in search and optimization,
Interdisciplinary statistics.
A.I. Propoi and A.V. Pukhlikov.
The stochastic Newton method in nonlinear extremal problems.
Avtomat. i Telemekh., 4:85-95, 1993.
A. V. Protasov and V. A. Ogorodnikov.
Dynamic probabilistic method of numerical modeling of stochastic
multidimensional fields.
In International Conference on Computational Mathematics. Part
I, II, pages 259-263. ICM&MG Pub., Novosibirsk, 2002.
I.M. Prudnikov.
A method for the global optimization of a function and an estimate
for the rate of its convergence.
Avtomat. i Telemekh., 12:72-81, 1993.
B.N. Pshenichnyj and V.G. Pokotilo.
On a linear object observation problem.
J. Appl. Math. Mech. 46, 156-160 translation from Prikl. Mat.
Mekh. 46, 212-217 (1982)., 1983.
V.A. Pukas.
Distribution of active loading in a power system as a two-stage
nonlinear stochastic programming problem.
Trudy Akad. Nauk Litov. SSR Ser. B, 5(84):147-156, 1974.
Madan L. Puri and Dan A. Ralescu.
Différentielle d'une fonction floue.
C.R. Acad. Sci. Paris Sér. I Math., 293(4):237-239, 1981.
M.C. Puri.
Extreme point enumeration technique for assignment problem.
Portugal. Math., 37(1-2):31-37 (1981), 1978.
Martin L. Puterman.
Markov decision processes.
In Stochastic models, Handb. Oper. Res. Manage. Sci. 2,
331-434, 1990.
Yu. P. Pyt'ev.
Nonlinear reduction of a measurement.
Mat. Model., 1(5):44-59, 158, 1989.
Li Qun Qi.
An alternating method for stochastic linear programming with simple
recourse.
Math. Programming Stud., 27:183-190, 1986.
Stochastic programming 84. I.
Li Qun Qi.
The A-forest iteration method for the stochastic generalized
transportation problem.
Math. Oper. Res., 12(1):1-21, 1987.
Li Qun Qi and Xiao Ming Tu.
The dual forest iteration method for stochastic transportation
problems.
J. Tsinghua Univ., 28(3):74-82, 1988.
Li Qun Qi and Robert S. Womersley.
An SQP algorithm for extended linear-quadratic problems in
stochastic programming.
Ann. Oper. Res., 56:251-285, 1995.
Stochastic programming (Udine, 1992).
Liqun Qi.
Forest iteration method for stochastic transportation problem.
Math. Program. Study 25, 142-163, 1985.
Shi Xian Qian.
Generalization of least-square isotonic regression.
J. Statist. Plann. Inference, 38(3):389-397, 1994.
Chang Ge Qiao.
Convergence analysis of a stochastic parallel algorithm.
J. Numer. Methods Comput. Appl., 17(4):308-312, 1996.
J. Qiu and A. A. Girgis.
Optimization of power-system reliability level by
stochastic-programming.
Electric Power Systems Research, 26(2):87-95, 1993.
J.P. Quadrat.
On optimal stochastic control problem of large systems.
In Advances in filtering and optimal stochastic control, Proc.
IFIP-WG 7/1 Work. Conf., Cocoyoc/Mex. 1982, Lect. Notes Contr. Inf. Sci. 42,
312-325 , 1982.
J.P. Quadrat and M. Viot.
Approximation numerique des problemes de programmation dynamique
stochastique.
IRIA, Cahier No.9, 201-224, 1972.
J.P. Quadrat and M. Viot.
Methodes de simulation en programmation dynamique stochastique.
Revue Franc. Automat. Inform. Rech. operat. 7, R-1, 3-22, 1973.
Guillaume Rabault.
When do borrowing constraints bind? Some new results on the income
fluctuation problem.
J. Econom. Dynam. Control, 26(2):217-245, 2002.
S.T. Rachev and W. Römisch, editors.
Stochastic Programming: Stability, Numerical Methods and
Applications.
1994.
J. of Computational and Applied Mathematics 56, No. 1-2.
Svetlozar T. Rachev and Werner Römisch.
Quantitative stability in stochastic programming: the method of
probability metrics.
Math. Oper. Res., 27(4):792-818, 2002.
Rüdiger Rackwitz.
Comparison of gradient-free optimizers in structural reliability
analysis.
In Stochastic optimization techniques (Neubiberg/Munich, 2000),
volume 513 of Lecture Notes in Econom. and Math. Systems, pages
309-319. Springer, Berlin, 2002.
S. Raczynski.
Stochastic optimization algorithm for nonlinear discrete models of
production systems.
Probl. Control Inf. Theory 7, 443-458 (Russian), Suppl. 1-14
(English), 1978.
F.J. Radermacher.
Cost-dependent essential systems of ES-strategies for stochastic
scheduling problems.
Methods Oper. Res. 42, 17-31, 1981.
A. Ye. Radiyevskiy.
A stochastic multicriterial optimization problem.
J. Automat. Inform. Sci., 25(4):28-30 (1993), 1992.
Yu. S. Ragimov.
On a decision-making problem in operations with random factors.
In Numerical methods in optimization and analysis (Russian)
(Irkutsk, 1989), pages 41-46. "Nauka" Sibirsk. Otdel., Novosibirsk, 1992.
Yu.S. Ragimov.
The principle of maximal guaranteed result in operations with random
factors.
Teor. Slozhnykh Sist. Metody Model. 1979, 64-76, 1979.
E. Raik.
Qualitative investigations in problems of stochastic nonlinear
programming.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat., 20:8-14,
1971.
E. Raik.
The quantile function in stochastic nonlinear programming.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat., 20:229-231,
1971.
E. Raik.
Problems of stochastic programming with probability and quantile
functionals.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat., 21:142-148,
1972.
E. Raik.
Stochastic programming problems with decision functions.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat., 21:258-263,
1972.
È. Raik.
Differentiability of a probability function with respect to a
parameter, and a stochastic pseudogradient method of optimizing it.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat., 24(1):5-9,
1975.
V. Raik, È.
The structure of optimal randomized solutions of stochastic
programming problems.
Kibernetika (Kiev), 2:95-98, 1976.
W.M. Raike.
Dissection methods for solutions in chance constrained programming
problems under discrete distributions.
Management Sci., Theory 16, 708-715, 1970.
Sanguthevar Rajasekaran and John H. Reif.
Nested annealing: a provable improvement to simulated annealing.
Theoret. Comput. Sci., 99(1):157-176, 1992.
Eh. Rajk.
Comparison of solutions in different formulations of stochastic
programming problems.
Izv. Akad. Nauk Ehst. SSR, Fiz. Mat. 19, 469-472, 1970.
Eh. Rajk.
Ungleichungen in Aufgaben der stochastischen Programmierung.
Izv. Akad. Nauk Ehst. SSR, Fiz. Mat. 19, 292-298, 1970.
Eh. Rajk.
On quantile function in stochastic nonlinear programming problems.
Izv. Akad. Nauk Ehst. SSR, Fiz. Mat. 20, 229-231, 1971.
Terry R. Rakes and Gary R. Reeves.
Selecting tolerances in chance-constrained programming: a multiple
objective linear programming approach.
Oper. Res. Lett., 4(2):65-69, 1985.
Katrien Ramaekers, Gerrit K. Janssens, and Hendrik Van Landeghem.
Towards logistics systems parameter optimisation through the use of
response surfaces.
4OR, 4(4):331-342, 2006.
Aparna Ramanathan and Charles J. Colbourn.
Bounds for all-terminal reliability by arc-packing.
Ars Combin., 23(A):229-236, 1987.
Jörg Rambau.
Deferment Control for Reoptimization - How to Find Fair Reoptimized
Dispatches.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Jaroslav Ramik and Josef Rimanek.
Vaguely formulated coefficients of linear inequalities.
BUSEFAL 15, 20-24, 1983.
Jaroslav Ramik and Josef Rimanek.
Fuzzy parameters in optimal allocation of resources.
In Optimization models using fuzzy sets and possibility theory,
Theory Decis. Libr., Ser. B 4, 359-374, 1987.
Ya. Ramik and J. Rimanek.
Linear constraints with inexact data.
Sov. J. Comput. Syst. Sci. 25, No.5, 90-98 translation from Izv.
Akad. Nauk SSSR, Tekh. Kibern. 1987, No.2, 41-48 (1987)., 1987.
J.A. Ramos.
A Kalman-tracking filter approach to nonlinear programming.
Comput. Math. Appl. 19, No.11, 63-74, 1990.
Oma Rani and R.N. Kaul.
Nonlinear programming in complex space.
J. math. Analysis Appl. 43, 1-14, 1973.
G.V. Rao.
On dynamic programming and risk functions.
Cahiers Centre Etud. Rech. oper. 14, 88-104, 1972.
P.K. Rao and K.S. Reddy.
Optimal economic growth under uncertainty with time-lags.
Internat. J. Systems Sci. 6, 793-797, 1975.
Shivaji Rao and George O.IV Schneller.
On the stochastic non-sequential production-planning problem.
J. Oper. Res. Soc. 41, No.3, 241-247, 1990.
Tamás Rapcsák.
An exterior-point algorithm for solution of convex nonlinear
programming problems.
Alkalmaz. Mat. Lapok, 1(3-4):357-364, 1975.
Tamas Rapcsak and Peter Borzsak.
On the concavity set of the product functions.
Alkalmazott Mat. Lapok 11, 311-318, 1985.
È. O. Rapoport.
Asymptotic properties of a test connected with a random partition of
an interval.
Optimizatsiya, 35(52):28-35, 158, 1985.
G. Rappl.
Optimierung durch Zufallsrichtungsverfahren., 1980.
L.A. Rastrigin.
Mixed algorithms of random search.
In Problems of random search, 2 (Russian), pages 8-17, 219.
Izdat. "Zinatne", Riga, 1973.
L.A. Rastrigin.
Particularities of the calculation of bounds in random search
processes.
Problemy Sluchain. Poiska, 7:13-21, 317, 1978.
Adaptation problems in technical systems (Russian).
L.A. Rastrigin.
Adaptation of complex systems. Methods and applications.
(Adaptatsiya slozhnykh sistem. Metody i prilozheniya).
Akademiya Nauk Latvijskoj SSR. Institut Ehlektroniki i
Vychislitel'noj Tekhniki. Riga: "Zinatne"., 1981.
L.A. Rastrigin.
Sequential binary random search in problems of stochastic
programming.
Soviet J. Comput. Systems Sci., 25(5):163-172, 1987.
L.A. Rastrigin and A.V. Samchenko.
Application of the mechanisms of natural evolution for the solution
of optimization problems.
Dinamika Sistem, 195:163-174, 1984.
R. Ravi and Amitabh Sinha.
Hedging uncertainty: approximation algorithms for stochastic
optimization problems.
In Integer programming and combinatorial optimization, volume
3064 of Lecture Notes in Comput. Sci., pages 101-115. Springer,
Berlin, 2004.
R. Ravi and Amitabh Sinha.
Hedging uncertainty: approximation algorithms for stochastic
optimization problems.
Math. Program., 108(1, Ser. A):97-114, 2006.
Daniel J. Reaume, H. Edwin Romeijn, and Robert L. Smith.
Implementing pure adaptive search for global optimization using
Markov chain sampling.
J. Global Optim., 20(1):33-47, 2001.
M. C. Recchioni and A. Scoccia.
A stochastic algorithm for constrained global optimization.
J. Global Optim., 16(3):257-270, 2000.
Giovanna Redaelli.
Convergence problems in stochastic programming models with
probabilistic constraints.
Riv. Mat. Sci. Econom. Social., 21(1-2):147-164, 1998.
Giovanna Redaelli.
Vector stochastic optimization problems.
In Generalized convexity and generalized monotonicity
(Karlovassi, 1999), volume 502 of Lecture Notes in Econom. and Math.
Systems, pages 362-380. Springer, Berlin, 2001.
R.-R. Redetzky.
Stabilitaetsanalyse der stochastischen linearen Optimierung (SLO).
I.
Wiss. Z. techn. Hochschule Ilmenau 22, 31-64, 1976.
R.-R. Redetzky.
Stabilitaetsanalyse der stochastischen linearen Optimierung (SLO).
II.
Wiss. Z. Techn. Hochschule Ilmenau 23, No.6, 111-135, 1977.
R.-R. Redetzky.
Der gegenwärtige Stand beim Verteilungsproblem der
stochastischen linearen Optimierung. I.
Wiss. Z. Tech. Hochsch. Ilmenau, 25(1):75-104, 1979.
R.-R. Redetzky.
Zum gegenwärtigen Stand beim Verteilungsproblem der
stochastischen linearen Optimierung. II.
Wiss. Z. Tech. Hochsch. Ilmenau, 27(1):109-141, 1981.
Robert Redetzky.
Ueber Dualitaetsaussagen in der stochastischen linearen Optimierung
(SLO). (On duality in stochastic linear optimization (SLO)).
Wiss. Z. Tech. Hochschule Ilmenau 16, No.5, 7-16, 1970.
R.R. Redetzky.
Verteilungsproblem der stochastischen linearen Optimierung.
In 21. int. wiss. Kolloq.; Ilmenau 1976, Heft 3, 81-84, 1976.
J. Reinhart.
Implementation of the response surface method (RSM) for
stochastic structural optimization problems.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 394-409. Springer, Berlin, 1998.
Juergen Th. Rembold.
Stochastische lineare Optimierung. Eine anwendungsbezogene
systematische Darstellung.
Mathematical Systems in Economics. 31. Meisenheim am Glan: Verlag
Anton Hain., 1977.
Juergen Th. Rembold.
Stochastische Engpassprobleme.
Technical report, Mathematisch-Naturwissenschaftliche Fakultaet der
Universitaet zu Koeln. 154 S., 1978.
Jürgen Th. Rembold.
Stochastische lineare Optimierung.
Verlag Anton Hain, Meisenheim am Glan, 1977.
Eine anwendungsbezogene systematische Darstellung, Mathematical
Systems in Economics, No. 31.
C. Renotte, A. Vande Wouwer, and M. Remy.
Neural modeling and control of a heat exchanger based on SPSA.
In Proceedings of the American Control Conference, pages
3299-3303, 2000.
L.K. Repina and V.N. Solncev.
Solution of two applied problems by the method of random search.
In Operations research and statistical modeling, No. 1
(Russian), pages 136-145. Izdat. Leningrad. Univ., Leningrad, 1972.
M. Resh.
Chance constrained programming of the machine loading problem with
stochastic processing times.
Management Sci., 17:48-65, 1970/71.
Michael Resh and Moshe Friedman.
Stochastic programming of multiple channel service systems with
deterministic inflow and stochastic service times.
Management Sci., 22(9):1022-1033, 1975/76.
C. ReVelle and K. Hogan.
A reliability-constrained siting model with local estimates of busy
fractions.
Environment and Planning B: Planning and Design,
15(3):143-152, 1988.
F. Rezayat.
On the use of an SPSA-based model-free controller in quality
improvement.
Automatica, 31:913-915, 1995.
F. Rezayat.
Constrained SPSA controller for operations processes.
IEEE Transactions on Systems, Man, and Cybernetics?A,
29:645-649, 1999.
K. Reznicek and T.C.E. Cheng.
Stochastic modelling of reservoir operations.
Eur. J. Oper. Res. 50, No.3, 235-248, 1991.
WanSoo T. Rhee.
Convergence of optimal stochastic bin packing.
Oper. Res. Lett. 4, 121-123, 1985.
Wansoo T. Rhee.
A note on asymptotic properties of the quadratic assignment
problem.
Oper. Res. Lett. 7, No.4, 197-200, 1988.
Wansoo T. Rhee.
Optimal bin packing with items of random sizes.
Math. Oper. Res. 13, No.1, 140-151, 1988.
WanSoo T. Rhee.
On the fluctuations of the stochastic traveling salesperson problem.
Math. Oper. Res., 16(3):482-489, 1991.
WanSoo T. Rhee.
Stochastic analysis of a modified first fit decreasing packing.
Math. Oper. Res., 16(1):162-175, 1991.
WanSoo T. Rhee.
Optimal bin packing of items of sizes uniformly distributed over
[0,1].
Math. Oper. Res. 18, No.3, 694-704, 1993.
Wansoo T. Rhee.
Probabilistic analysis of a capacitated vehicle routing problem. I.
Optimization 27, No.1-2, 79-87, 1993.
WanSoo T. Rhee and Michel Talagrand.
Martingale inequalities, interpolation and NP-complete problems.
Math. Oper. Res., 14(1):91-96, 1989.
WanSoo T. Rhee and Michel Talagrand.
Dual bin packing with items of random sizes.
Math. Programming, 58(2, Ser. A):229-242, 1993.
Wansoo T. Rhee and Michel Talagrand.
On-line bin packing of items of random sizes. II.
SIAM J. Comput. 22, No.6, 1251-1256, 1993.
WanSoo T. Rhee and Michel Talagrand.
On-line bin packing with items of random size.
Math. Oper. Res., 18(2):438-445, 1993.
W.T. Rhee.
Inequalities for bin packing. III.
Optimization 29, No.4, 381-385, 1994.
W.T. Rhee and M. Talagrand.
Multidimensional optimal bin packing with items of random size.
Mathematics of Operations Research, 16(3):490-503, 1991.
Ulrich Rieder and Rudi Zagst.
Monotonicity and bounds for convex stochastic control models.
Z. Oper. Res., 39(2):187-207, 1994.
Rhonda Righter.
Job scheduling to minimize expected weighted flowtime on uniform
processors.
Syst. Control Lett. 10, No.4, 211-216, 1988.
Rhonda Righter.
Stochastically maximizing the number of successes in a sequential
assignment problem.
J. Appl. Probab., 27(2):351-364, 1990.
Rhonda Righter and Susan H. Xu.
Scheduling jobs on non-identical IFR processors to minimize general
cost functions.
Adv. Appl. Probab. 23, No.4, 909-924, 1991.
Morten Riis and Kim Allan Andersen.
Applying the minimax criterion in stochastic recourse programs.
European J. Oper. Res., 165(3):569-584, 2005.
Morten Riis and Jørn Lodahl.
A bicriteria stochastic programming model for capacity expansion in
telecommunications.
Math. Methods Oper. Res., 56(1):83-100, 2002.
Special issue on combinatorial and integer programming.
Morten Riis and Rüdiger Schultz.
Applying the minimum risk criterion in stochastic recourse programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2001.
A.H.G. Rinnooy Kan.
Stochastic integer programming: the distribution problem.
In Stochastic programming (Gargnano, 1983), volume 76 of
Lecture Notes in Control and Inform. Sci., pages 140-150, Berlin, 1986.
Springer.
A.H.G. Rinnooy Kan and L. Stougie.
Stochastic integer programming.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 201-213. Springer, Berlin, 1988.
A.H.G. Rinnooy Kan and G.T. Timmer.
Stochastic methods for global optimization.
Amer. J. Math. Management Sci., 4(1-2):7-40, 1984.
Statistics and optimization : the interface.
Alexander H.G. Rinnooy Kan and Leen Stougie.
On the relation between complexity and uncertainty.
Ann. Oper. Res. 18, No.1-4, 17-23, 1989.
Yosef Rinott.
Convexity and optimization in certain problems in statistics.
In Recent results in stochastic programming, Proc., Oberwolfach
1979, Lect. Notes Econ. Math. Syst. 179, 99-103, 1980.
K.K. Ripa.
Some statistic properties of optimizing automata and random search.
Avtomat. Vychisl. Tekh., Riga 1970, No.3, 28-32, 1970.
K.K. Ripa.
Random search for the extremum of a multidimensional object as a
stochastic automaton.
In Problems of statistical optimization (Russsian), pages
15-30. Izdat. "Zinatne", Riga, 1971.
K.K. Ripa.
A generalized algorithm of coordinate-wise adaptation of random
search for the extremum.
Problemy Sluchain. Poiska, 7:135-148, 318, 1978.
Adaptation problems in technical systems (Russian).
K.K. Ripa.
A two-level algorithm of the adaptation of a random search for the
extremum.
Problemy Sluchain. Poiska, 7:159-183, 319, 1978.
Adaptation problems in technical systems (Russian).
Klaus Ritter.
Approximation and optimization on the Wiener space.
J. Complexity, 6(4):337-364, 1990.
Klaus Ritter and Stefan Schäffler.
A stochastic method for constrained global optimization.
SIAM J. Optim., 4(4):894-904, 1994.
Gianfranco Rizzo and Cesare Pianese.
A stochastic approach for the optimization of open-loop engine
control systems.
Ann. Oper. Res., 31(1-4):545-568, 1991.
Stochastic programming, Part II (Ann Arbor, MI, 1989).
Werner Römisch Robert Nürnberg.
A two-stage planning model for power scheduling in a hydro-thermal
system under uncertainty.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
S.M. Robinson.
Local structure of feasible sets in nonlinear programming, Part
III: Stability and sensitivity.
Mathematical Programming Study, 30:45-66, 1987.
Stephen M. Robinson.
Extended scenario analysis.
Ann. Oper. Res., 31(1-4):385-397, 1991.
Stochastic programming, Part II (Ann Arbor, MI, 1989).
Stephen M. Robinson.
Analysis of sample-path optimization.
Math. Oper. Res., 21(3):513-528, 1996.
Stephen M. Robinson and Roger J.-B. Wets.
Stability in two-stage stochastic programming.
SIAM J. Control Optim., 25(6):1409-1416, 1987.
R. T. Rockafellar.
Duality and optimality in multistage stochastic programming.
Ann. Oper. Res., 85:1-19, 1999.
Stochastic programming. State of the art, 1998 (Vancouver, BC).
R. Tyrrell Rockafellar, Stan Uryasev, and Michael Zabarankin.
Optimality conditions in portfolio analysis with general deviation
measures.
Math. Program., 108(2-3, Ser. B):515-540, 2006.
R. Tyrrell Rockafellar and Roger J.B. Wets.
Continuous versus measurable recourse in N-stage stochastic
programming.
J. Math. Anal. Appl., 48:836-859, 1974.
R.T. Rockafellar.
Lagrange multipliers for an N-stage model in stochastic convex
programming.
In Analyse convexe Appl., Lecture Notes Econ. math. Syst. 102,
180-187 , 1974.
R.T. Rockafellar.
On the equivalence of multistage recourse models in stochastic
optimization.
In Control theory, numerical methods and computer systems
modelling (Internat. Sympos., IRIA LABORIA, Rocquencourt, 1974), pages
314-321. Lecture Notes in Econom. and Math. Systems, Vol. 107, Berlin, 1975.
Springer.
R.T. Rockafellar.
Computational schemes for large-scale problems in extended linear-
quadratic programming.
Math. Program., Ser. B 48, No.3, 447-474, 1990.
R.T. Rockafellar and R.J.-B. Wets.
Stochastic convex programming: Kuhn-Tucker conditions.
J. Math. Econom., 2(3):349-370, 1975.
R.T. Rockafellar and R.J.-B. Wets.
Nonanticipativity and l1-martingales in stochastic optimization
problems.
Math. Programming Stud., 6:170-187, 1976.
Stochastic systems: modeling, identification and optimization, II
(Proc. Sympos., Univ Kentucky, Lexington, Ky., 1975).
R.T. Rockafellar and R.J.-B. Wets.
Stochastic convex programming: basic duality.
Pacific J. Math., 62(1):173-195, 1976.
R.T. Rockafellar and R.J.-B. Wets.
Stochastic convex programming: relatively complete recourse and
induced feasibility.
SIAM J. Control Optimization, 14(3):574-589, 1976.
R.T. Rockafellar and R.J.-B. Wets.
Stochastic convex programming: singular multipliers and extended
duality singular multipliers and duality.
Pacific J. Math., 62(2):507-522, 1976.
R.T. Rockafellar and R.J.-B. Wets.
Measures as Lagrange multipliers in multistage stochastic
programming.
J. Math. Anal. Appl., 60(2):301-313, 1977.
R.T. Rockafellar and R.J.-B. Wets.
The optimal recourse problem in discrete time:
L\sp1-multipliers for inequality constraints.
SIAM J. Control Optimization, 16(1):16-36, 1978.
R.T. Rockafellar and R.J.-B. Wets.
Deterministic and stochastic optimization problems of Bolza type in
discrete time.
Stochastics, 10(3-4):273-312, 1983.
R.T. Rockafellar and R.J.-B. Wets.
A Lagrangian finite generation technique for solving
linear-quadratic problems in stochastic programming.
Math. Programming Stud., 28:63-93, 1986.
Stochastic programming 84. II.
R.T. Rockafellar and R.J.-B. Wets.
Linear-quadratic programming problems with stochastic penalties: The
finite generation algorithm.
In Stochastic optimization, Proc. Int. Conf., Kiev/USSR 1984,
Lect. Notes Control Inf. Sci. 81, 545-560, 1986.
R.T. Rockafellar and R.J.-B. Wets.
A note about projections in the implementation of stochastic
quasigradient methods.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 385-392. Springer, Berlin, 1988.
R.T. Rockafellar and R.J.-B. Wets.
Generalized linear-quadratic problems of deterministic and stochastic
optimal control in discrete time.
SIAM J. Control Optim., 28(4):810-822, 1990.
R.T. Rockafellar and Roger J.-B. Wets.
A dual solution procedure for quadratic stochastic programs with
simple recourse.
In Numerical methods (Caracas, 1982), volume 1005 of
Lecture Notes in Math., pages 252-265. Springer, Berlin, 1983.
R.T. Rockafellar and Roger J.-B. Wets.
Scenarios and policy aggregation in optimization under uncertainty.
Math. Oper. Res., 16(1):119-147, 1991.
R.T. Rockafellar and Roger J.-B. Wets.
A dual strategy for the implementation of the aggregation principle
in decision making under uncertainty.
Appl. Stochastic Models Data Anal., 8(3):245-255, 1992.
N. Ye Rodnishchev.
Method of feasible directions in optimization problems for stochastic
systems.
Engrg. Cybernetics, 10(1):29-34, 1972.
J.R. Rodriguez-Mancilla and William T. Ziemba.
The duality of option investment strategies for hedge funds.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
W. Roedder.
Ein Loesungsvorschlag fuer ein stochastisches Zielprogramm.
In Operations Res.-Verf. 13, IV. Oberwolfach-Tag. Operations
Res. 1971, 321- 328, 1972.
W. Roedder.
Loesungsvorschlaege fuer stochastische Zielprogramme.
In Proc. Oper. Res., DGU Ann. Meet. 1971, 166-176, 1972.
W. Roemisch and R. Schultz.
Quantitative stability of two-stage stochastic programs.
Z. Angew. Math. Mech. 74, No.6, T587-T589, 1994.
Werner Roemisch.
An approximation method in stochastic optimization and control.
In Mathematical control theory, Banach Cent. Publ. 14,
477-490, 1985.
N.V. Roenko.
A stochastic problem of optimal placement of objects.
Issled. Operatsii i ASU, 23:19-26, 139, 1984.
H. Edwin Romeijn.
Global optimization by random walk sampling methods, volume 32
of Tinbergen Institute Research Series.
Thesis Publishers, Amsterdam, 1992.
H. Edwin Romeijn and Dolores Romero Morales.
A probabilistic analysis of the multi-period single-sourcing problem.
Discrete Appl. Math., 112(1-3):301-328, 2001.
Combinatorial Optimization Symposium (Brussels, 1998).
H. Edwin Romeijn and Nanda Piersma.
A probabilistic feasibility and value analysis of the generalized
assignment problem.
J. Comb. Optim., 4(3):325-355, 2000.
W. Römisch.
On the convergence of measurable selections and an application to
approximations in stochastic optimization.
Z. Anal. Anwendungen, 5(3):277-288, 1986.
W. Römisch and R. Schultz.
Distribution sensitivity for certain classes of chance-constrained
models with application to power dispatch.
J. Optim. Theory Appl., 71(3):569-588, 1991.
W. Römisch and R. Schultz.
Multistage stochastic integer programs: An introduction.
In M. Grötchel, S.O. Krumke, and J. Rambau, editors, Online
Optimization of Large Scale Systems, pages 581-600. Springer, Berlin, 2001.
Werner Römisch.
On discrete approximations in stochastic programming.
In Proceedings of the 13th Annual Conference on Mathematical
Optimization (Berlin, 1981), volume 39 of Seminarberichte, pages
166-175. Humboldt Univ. Berlin, 1982.
Werner Römisch.
On convergence rates of approximations in stochastic programming.
In 17. Jahrestagung "Mathematische Optimierung" (Sellin,
1984), volume 80 of Seminarberichte, pages 82-91. Humboldt Univ.
Berlin, 1986.
Werner Römisch.
Stability analysis of stochastic programs.
Investigación Oper., 14(2-3):175-194, 1993.
Workshop on Stochastic Optimization: the State of the Art (Havana,
1992).
Werner Römisch and Rüdiger Schultz.
On distribution sensitivity in chance constrained programming.
In Advances in mathematical optimization, volume 45 of
Math. Res., pages 161-168, Berlin, 1988. Akademie-Verlag.
Werner Römisch and Rüdiger Schultz.
Stochastic programs with complete recourse: stability and an
application to power dispatch.
In System modelling and optimization (Leipzig, 1989), volume
143 of Lecture Notes in Control and Inform. Sci., pages 688-696.
Springer, Berlin, 1990.
Werner Römisch and Rüdiger Schultz.
Distribution sensitivity in stochastic programming.
Math. Programming, 50(2, (Ser. A)):197-226, 1991.
Werner Römisch and Rüdiger Schultz.
Stability analysis for stochastic programs.
Ann. Oper. Res., 30(1-4):241-266, 1991.
Stochastic programming, Part I (Ann Arbor, MI, 1989).
Werner Römisch and Rüdiger Schultz.
Stability of solutions for stochastic programs with complete
recourse.
Math. Oper. Res., 18(3):590-609, 1993.
Werner Römisch and Rüdiger Schultz.
Lipschitz stability for stochastic programs with complete recourse.
SIAM J. Optim., 6(2):531-547, 1996.
Werner Römisch and Rüdiger Schultz.
*Multistage stochastic integer programs: an introduction.
In Online optimization of large scale systems, pages 581-622.
Springer, Berlin, 2001.
Werner Römisch and Anton Wakolbinger.
Obtaining convergence rates for approximations in stochastic
programming.
In Parametric optimization and related topics (Plaue, 1985),
volume 35 of Math. Res., pages 327-343. Akademie-Verlag, Berlin, 1987.
Werner Römisch and Roger J.-B. Wets.
Stability of e-approximate solutions to convex stochastic
programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2006.
Heinrich Rommelfanger.
Entscheiden bei Unschaerfe. Fuzzy Decision Support-Systeme.
Springer-Verlag, 1988.
Heinrich Rommelfanger.
Stochastic programming with vague data.
Ann. Univ. Sci. Budapest. Sect. Comput., 12:213-221, 1991.
Third IFSA-EC and EURO-WGFS Workshop on Fuzzy Sets (Visegrád,
1990).
Heinrich Rommelfanger.
Fuzzy mathematical programming. Modelling of vague data by fuzzy
sets and solution procedures.
In Bandemer, Hans (ed.), Modelling uncertain data. Berlin:
Akademie Verlag, (ISBN 3-05-501578-9/pbk). Math. Res. 68, 142-152, 1992.
Heinrich Rommelfanger.
Entscheiden bei Unschaerfe. Fuzzy Decision Support-Systeme.
(Decision- making in case of uncertainty. Decision-making in case of
uncertainty).2., verb. u. erw. Aufl.
Springer-Verlag, 1994.
Mohammad Roosta.
Routing through a network with maximum reliability.
J. Math. Anal. Appl. 88, 341-347, 1982.
Charles H. Rosa and Andrzej Ruszczy\'nski.
On augmented Lagrangian decomposition methods for multistage
stochastic programs.
Ann. Oper. Res., 64:289-309, 1996.
Stochastic programming, algorithms and models (Lillehammer, 1994).
Charles H. Rosa and Samer Takriti.
Improving aggregation bounds for two-stage stochastic programs.
Oper. Res. Lett., 24(3):127-137, 1999.
Wanda Rosa-Hatko and Eldon Gunn.
Some models of queueing control with switchover.
Comput. Math. Appl. 21, No.11/12, 91-110, 1991.
Scott L. Rosen and Catherine M. Harmonosky.
An improved simulated annealing simulation optimization method for
discrete parameter stochastic systems.
Comput. Oper. Res., 32(2):343-358, 2005.
Sheldon Ross.
Introduction to stochastic dynamic programming.
Probability and Mathematical Statistics. New York - London etc.:
Academic Press., 1983.
Francesco A. Rossi and Ilario Gavioli.
Aspects of heuristic methods in the "Probabilistic Traveling
Salesman Problem" (PTSP).
In Stochastic in combinatorial optimization, Adv. Sch. CISM,
Udine/Italy 1986, 214-227, 1987.
V.I. Rotar'.
Symmetric dependence, and a certain model for sample inspection.
Èkonom. i Mat. Metody, 9:1150-1156, 1973.
V.I. Rotar'.
On sufficient controls in dynamical problems of stochastic
optimization.
Mat. Zametki 40, No.4, 542-551, 1986.
Marc Roubens and Jr. Teghem, Jacques.
Comparison of methodologies for multicriteria
feasibility-constrained fuzzy and multiple-objective stochastic linear
programming.
In Combining fuzzy imprecision with probabilistic uncertainty in
decision making, volume 310 of Lecture Notes in Econom. and Math.
Systems, pages 240-265. Springer, Berlin, 1988.
Marc Roubens and Jr. Teghem, Jacques.
Comparison of methodologies for fuzzy and stochastic multi-objective
programming.
Fuzzy Sets and Systems, 42(1):119-132, 1991.
B. Roy.
Problems and methods with multiple objective functions.
Math. Programming 1, 239-266, 1971.
Santanu Roy.
A note: "On income fluctuations and capital gains with a convex
production function" [J. Econom. Dynam. Control 11 (1987),
no. 3, 285-312; MR 88j:90067] by M. O. Sotomayor.
J. Econom. Dynam. Control, 18(6):1199-1202, 1994.
J. O. Royset and E. Polak.
Implementable algorithm for stochastic optimization using sample
average approximations.
J. Optim. Theory Appl., 122(1):157-184, 2004.
Yu. A. Rozanov.
On optimization of random functionals.
In Control theory, numerical methods and computer systems
modelling (Internat. Sympos., IRIA LABORIA, Rocquencourt, 1974), pages
192-206. Lecture Notes in Econom. and Math. Systems, Vol. 107, Berlin, 1975.
Springer.
Yuri A. Rozanov.
A few methodological remarks on optimization random cost functions.
Research memorandum. rm-73-7., Laxenburg, Austria: IIASA -
International Institute for Applied Systems Analysis., 1973.
G.B. Rubal'skij.
Convolution operators conserving the property of the unimodality
type for functions of one discrete variable.
Cybernetics 24, No.3, 281-285 translation from Kibernetika 1988,
No.3, 9-11 (1988)., 1988.
E.Ya. Rubinovich.
Trajectory control of observations in discrete stochastic
optimization problems.
Autom. Remote Control 41, 365-372 translation from Avtom.
Telemekh. 1980, No.3, 93-102 (1980)., 1980.
Ja. S. Rubinstein.
The piecewise-linear representation of a function that is optimized
in a noise environment.
Avtomat. i Vycisl. Tehn., 5:36-43, 1968.
Ja. S. Rubinstein and È. K. Spilevskii.
Asymptotic properties of a random search that is to be used for a
search for the extremum of a function under conditions of noise.
Voprosy Kibernet. Vycisl. Mat., 28:178-187, 1969.
Problems of statistical optimization (Proc. Fifth All-Union Sem.,
Tashkent, 1968).
Reuven Y. Rubinstein.
Monte Carlo optimization, simulation and sensitivity of
queueing networks.
Wiley Series in Probability and Mathematical Statistics: Applied
Probability and Statistics. New York etc.: John Wiley & Sons, Inc., 1986.
Reuven Y. Rubinstein.
Combinatorial optimization, cross-entropy, ants and rare events.
In Stochastic optimization: algorithms and applications
(Gainesville, FL, 2000), pages 303-363. Kluwer Acad. Publ., Dordrecht,
2001.
Reuven Y. Rubinstein and Alexander Shapiro.
Discrete event systems.
Wiley Series in Probability and Mathematical Statistics: Probability
and Mathematical Statistics. John Wiley & Sons Ltd., Chichester, 1993.
Sensitivity analysis and stochastic optimization by the score
function method.
R.Y. Rubinstein.
Solution of nonlinear programming with unknown distribution function.
Math. Comput. Simulation, 24(5):373-384, 1982.
R.Y. Rubinstein, S. Shapiro, and S. Uryasev.
The score functions.
In S.I. Gass and C.M. Harris, editors, Encyclopedia of
Operations Research and Management Science, pages 614-617. Kluwer Academic
Publishers, Boston/Dordrecht/London, 1996.
Y. Rubinstein and A. Karnovsky.
Local and integral properties of a search algorithm of the
stochastic approximation type.
Stochastic Processes Appl. 6, 124-134, 1978.
Y. Rubinstein and A. Karnovsky.
The regenerative method for constrained optimization problems.
In Operational research '78 (Proc. Eighth IFORS Internat. Conf.,
Toronto, Ont., 1978), pages 931-949. North-Holland, Amsterdam, 1979.
Y.R. Rubinstein and G. Samorodnitsky.
Efficiency of the random search method.
Math. Comput. Simulation, 24(4):257-268, 1982.
Ludger Rüschendorf and Ludger Uckelmann.
Numerical and analytical results for the transportation problem of
Monge-Kantorovich.
Metrika, 51(3):245-258 (electronic), 2000.
Berc Rustem.
Methods for optimal economic policy design.
In Control and dynamic systems, Vol. 36, pages 17-74.
Academic Press, San Diego, CA, 1990.
Ioan Rusu.
On the solutions of a certain stochastic programming problem.
In Proceedings of the Fourth Conference on Probability Theory
(Bra sov, 1971), pages 583-586. Editura Acad. R.S.R., Bucharest, 1973.
Ioan Rusu and Filia Co tiu.
Sur un problème de programmation linéaire stochastique.
In Transactions of the Seventh Prague Conference on Information
Theory, Statistical Decision Functions and the Eighth European Meeting of
Statisticians (Tech. Univ, pages 457-463. Academia, Prague, 1978.
A. Ruszczy\'nski.
A regularized decomposition method for minimizing a sum of polyhedral
functions.
Mathematical Programming, 35:309-333, 1986.
A. Ruszczynski and A. Shapiro, editors.
Stochastic Programming, volume 10 of Handbooks in
Operations Research and Management Science.
Elsevier, 2003.
Andrzej Ruszczy\'nski.
Feasible direction methods for stochastic programming problems.
Math. Programming, 19(2):220-229, 1980.
Andrzej Ruszczy\'nski.
Stochastic feasible direction methods for nonsmooth stochastic
optimization problems.
Control Cybernet., 9(4):173-187 (1981), 1980.
Andrzej Ruszczy\'nski.
A recursive quadratic programming algorithm for constrained
stochastic programming problems.
Control Cybernet., 13(1-2):59-72, 1984.
Andrzej Ruszczy\'nski.
A method of feasible directions for solving nonsmooth stochastic
programming problems.
In Stochastic programming (Gargnano, 1983), volume 76 of
Lecture Notes in Control and Inform. Sci., pages 258-271. Springer, Berlin,
1986.
Andrzej Ruszczy\'nski.
A linearization method for nonsmooth stochastic programming problems.
Math. Oper. Res., 12(1):32-49, 1987.
Andrzej Ruszczy\'nski.
Modern techniques for linear dynamic and stochastic programs.
In Aspiration based decision support systems, volume 331 of
Lecture Notes in Econom. and Math. Systems, pages 48-67. Springer,
Berlin, 1989.
Andrzej Ruszczy\'nski.
Parallel decomposition of multistage stochastic programming problems.
Math. Programming, 58(2, Ser. A):201-228, 1993.
Andrzej Ruszczy\'nski.
On convergence of an augmented Lagrangian decomposition method for
sparse convex optimization.
Math. Oper. Res., 20(3):634-656, 1995.
Andrzej Ruszczy\'nski.
On regularized duality in convex optimization.
In Recent advances in nonsmooth optimization, pages 381-391,
River Edge, NJ, 1995. World Sci. Publishing.
Andrzej Ruszczy\'nski.
On the regularized decomposition method for stochastic programming
problems.
In Stochastic programming (Neubiberg/München, 1993), volume
423 of Lecture Notes in Econom. and Math. Systems, pages 93-108.
Springer, Berlin, 1995.
Andrzej Ruszczy\'nski.
Decomposition methods in stochastic programming.
Math. Programming (Ser. B), 79(1-3):333-353, 1997.
Lectures on mathematical programming (ismp97) (Lausanne, 1997).
Andrzej Ruszczy\'nski.
Some advances in decomposition methods for stochastic linear
programming.
Ann. Oper. Res., 85:153-172, 1999.
Stochastic programming. State of the art, 1998 (Vancouver, BC).
Andrzej Ruszczy\'nski.
Probabilistic programs with discrete distributions and precedence
constrained knapsack polyhedra.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Andrzej Ruszczy\'nski.
Probabilistic programming with discrete distributions and precedence
constrained knapsack polyhedra.
Math. Program., 93(2, Ser. A):195-215, 2002.
Andrzej Ruszczy\'nski and Alexander Shapiro.
Conditional risk mappings.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Andrzej Ruszczy\'nski and Alexander Shapiro.
Optimization of convex risk functions.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Andrzej Ruszczy\'nski and Alexander Shapiro.
Optimization of convex risk functions.
Math. Oper. Res., 31(3):433-452, 2006.
Andrzej Ruszczy\'nski and Alexander Shapiro.
Corrigendum to: "Optimization of convex risk functions" [Math.
Oper. Res. 31 (2006), no. 3, 433-452; mr2254417].
Math. Oper. Res., 32(2):496, 2007.
Andrzej Ruszczy\'nski and Wojciech Syski.
A method of aggregate stochastic subgradients with on-line stepsize
rules for convex stochastic programming problems.
Math. Programming Stud., 28:113-131, 1986.
Stochastic programming 84. II.
Andrzej Ruszczynski and Robert J. Vanderbei.
Frontiers of stochastically nondominated portfolios.
Stochastic Programming E-Print Series, http://www.speps.org,
2002.
David P. Rutenberg.
Risk aversion in stochastic programming with recourse.
Operations Res. 21, 377-380, 1973.
V.I. Rymaruk.
Stability of a stochastic programming method.
Issled. Operatsii i ASU, 24:11-13, 110, 1984.
V.I. Rymaruk.
Stability of a class of quasigradient methods of solving minimax
problems.
In Methods in operations research and reliability theory, pages
3-6, 63, Kiev, 1985. Akad. Nauk Ukrain. SSR Inst. Kibernet.
Oma M. Saad and Osama E. Imam.
On the solution of stochastic multiobjective integer linear
programming problems with a parametric study.
Optimization Online, http://www.optimization-online.org, 2007.
R.S. Sachan.
Stochastic programming problems under risk and uncertainty.
Cah. Cent. Etud. Rech. Oper. 12, 211-232, 1970.
P. Sadegh and J.C. Spall.
Optimal random perturbations for multivariate stochastic
approximation using a simultaneous perturbation gradient approximation.
In Proceedings of the American Control Conference, pages
3582-3586, 1997.
P. Sadegh and J.C. Spall.
Optimal sensor configuration for complex systems.
In Proceedings of the American Control Conference, pages
3575-3579, 1998.
Payman Sadegh.
Constrained optimization via stochastic approximation with a
simultaneous perturbation gradient approximation.
Automatica J. IFAC, 33(5):889-892, 1997.
K.V. Sagatelyan.
A probabilistic model of decision making.
Erevan. Gos. Univ. Uchen. Zap. Estestv. Nauki, 3(166):16-21
(1988), 1987.
Kemal H. Sahin and Urmila M. Diwekar.
Better optimization of nonlinear uncertain systems (BONUS): a new
algorithm for stochastic programming using reweighting through kernel density
estimation.
Ann. Oper. Res., 132:47-68, 2004.
N. P. Sahoo and M. P. Biswal.
Computation of probabilistic linear programming problems involving
normal and log-normal random variables with a joint constraint.
Int. J. Comput. Math., 82(11):1323-1338, 2005.
N. P. Sahoo and M. P. Biswal.
Computation of some stochastic linear programming problems with
Cauchy and extreme value distributions.
Int. J. Comput. Math., 82(6):685-698, 2005.
Gorkem Saka, Andrew J. Schaefer, and Lewis Ntaimo.
Inverse stochastic linear programming.
Optimization Online, http://www.optimization-online.org, 2007.
Minoru Sakaguchi.
A sequential assignment problem for randomly arrivin g jobs.
Rep. statist. Appl. Res., Un. Japan Sci. Engin. 19, 99-109,
1972.
Minoru Sakaguchi and Vesa Saario.
A class of best-choice problems with full information.
Math. Japon., 41(2):389-398, 1995.
L. Sakalauskas.
Towards implementable nonlinear stochastic programming.
In Coping with uncertainty, volume 581 of Lecture Notes in
Econom. and Math. Systems, pages 257-279. Springer, Berlin, 2006.
L. Sakalauskas and K. Zilinskas.
Application of the Monte-Carlo method to stochastic linear
programming.
In Computer aided methods in optimal design and operations,
volume 7 of Ser. Comput. Oper. Res., pages 39-48. World Sci. Publ.,
River Edge, NJ, 2006.
Leonidas Sakalauskas.
Nonlinear stochastic optimization by the Monte-Carlo method.
Informatica (Vilnius), 11(4):455-468, 2000.
Leonidas Sakalauskas.
Application of the Monte-Carlo method to nonlinear stochastic
optimization with linear constraints.
Informatica (Vilnius), 15(2):271-282, 2004.
Leonidas L. Sakalauskas.
Nonlinear stochastic programming by Monte-Carlo estimators.
European J. Oper. Res., 137(3):558-573, 2002.
L.L. Sakalauskas.
On the convergence of a centering procedure.
Litovsk. Mat. Sb., 31(4):670-677, 1991.
Masatoshi Sakawa and Fumiko Seo.
Interactive multiobjective decision-making in environmental systems
using the fuzzy sequential proxy optimization technique.
Large Scale Syst. 4, 223-243, 1983.
Yu. P. Sakharov and A.P. Akulov.
A method for nonlinear global optimization under conditions of
uncertainty.
Soobshch. Akad. Nauk Gruzin. SSR, 134(2):285-288, 1989.
Abdellah Salhi, L. G. Proll, D. Rios Insua, and J. I. Martin.
Experiences with stochastic algorithms for a class of constrained
global optimisation problems.
RAIRO Oper. Res., 34(2):183-197, 2000.
G. Salinetti.
Approximations for chance-constrained programming problems.
Stochastics, 10(3-4):157-179, 1983.
G. Salinetti.
Convergence of stochastic infima: equi-semicontinuity.
In Stochastic optimization (Kiev, 1984), volume 81 of
Lecture Notes in Control and Inform. Sci., pages 561-575. Springer, Berlin,
1986.
G. Salinetti.
Semicontinuous random functions and random measures.
Rend. Sem. Mat. Fis. Milano, 57:465-482 (1989), 1987.
G. Salinetti.
Stochastic optimization and stochastic processes: the epigraphical
approach.
In Parametric optimization and related topics (Plaue, 1985),
volume 35 of Math. Res., pages 344-354. Akademie-Verlag, Berlin, 1987.
G. Salinetti, W. Vervaat, and R.J.-B. Wets.
On the convergence in probability of random sets (measurable
multifunctions).
Math. Oper. Res., 11(3):420-422, 1986.
Gabriella Salinetti.
Consistency of statistical estimators: the epigraphical view.
In Stochastic optimization: algorithms and applications
(Gainesville, FL, 2000), pages 365-383. Kluwer Acad. Publ., Dordrecht,
2001.
Gabriella Salinetti and Roger J.-B. Wets.
On the convergence in distribution of measurable multifunctions
(random sets), normal integrands, stochastic processes and stochastic infima.
Math. Oper. Res., 11(3):385-419, 1986.
David H. Salinger and R. Tyrrell Rockafellar.
Dynamic splitting.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Paul A. Samuelson.
Mathematics of speculative price.
SIAM Review 15, 1-42, 1973.
M. Sanchez, M.A. Gonzalez, and J. Sicilia.
A model of a multivariable inventory.
Rev. Acad. Canar. Cienc. 1, 201-215, 1990.
M. Sánchez García and C. González Martín.
A global optimization algorithm based on the geometric probabilistic
model. Application to a classification problem.
Rev. Acad. Canaria Cienc., 3(1):33-44, 1991.
Peter Sanders, Naveen Sivadasan, and Martin Skutella.
Online Scheduling with Bounded Migration.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
A. P. Sanghvi and I. H. Shavel.
Investment planning for hydro-thermal power systems expansion:
Stochastic programming employing the Dantzig-Wolfe decomposition
principle.
IEEE Transactions on Power Systems, 1(2):115-121, 1986.
G. Santharam, P.S. Sastry, and M.A.L. Thathachar.
Continuous action set learning automata for stochastic optimization.
J. Franklin Inst. B, 331(5):607-628 (1995), 1994.
Tjendera Santoso, Shabbir Ahmed, Marc Goetschalckx, and Alexander Shapiro.
A stochastic programming approach for supply chain network design
under uncertainty.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Tjendera Santoso, Shabbir Ahmed, Marc Goetschalckx, and Alexander Shapiro.
A stochastic programming approach for supply chain network design
under uncertainty.
European J. Oper. Res., 167(1):96-115, 2005.
M.S. Sarma.
On the convergence of the Baba and Dorea random optimization
methods.
J. Optim. Theory Appl., 66(2):337-343, 1990.
Hueseyin Sarper.
Evaluation of the accuracy of Naslund's approximation for managerial
decision making under uncertainty.
Appl. Math. Comput. 55, No.1, 73-87, 1993.
Hüseyin Sarper.
Monte Carlo simulation for analysis of the optimum value
distribution in stochastic mathematical programs.
Math. Comput. Simulation, 35(6):469-480, 1993.
Tadeusz Sawik.
Stochastic programming models of production-inventory problems.
Arch. Autom. Telemech. 23, 115-127, 1978.
Anureet Saxena.
A short note on the probabilistic set covering problem.
Stochastic Programming E-Print Series, http://www.speps.org,
2007.
Anureet Saxena, Vineet Goyal, and Miguel Lejeune.
Mip reformulations of the probabilistic set covering problem.
Stochastic Programming E-Print Series, http://www.speps.org,
2007.
N.I. Scedrin and A.N. Karhov.
Mathematische Methoden der Programmierung in der Oekonomik.
(Matematiceskie metody programmirovanija v ekonomike.).
Matematiceskaja statistika dlja ekonomistov. Moskau: 'Statistika'.,
1974.
Manfred Schael.
Utility functions and optimal policies in sequential decision
problems.
In Game theory and mathematical economics, Proc. Semin.,
Bonn/Hagen 1980, 357-365, 1981.
Manfred Schael.
On dynamic programming under uncertainty.
In Operations research, Proc. 10th Annu. Meet., Goettingen
1981, 415-422 , 1982.
Manfred Schael.
Markovian decision models with bounded finite-stage rewards.
In Operations research, Proc. 12th Annu. Meet., Mannheim 1983,
470-474 , 1984.
Manfred Schael.
On stochastic dynamic programming: A bridge between Markov decision
processes and gambling.
In Markov processes and control theory, Proc. Symp., ISAM,
Gaussig/GDR 1988, Math. Res. 54, 178-216, 1989.
Guido Schäfer and Naveen Sivadasan.
Topology Matters: Smoothed Competitiveness of Metrical Task
Systems.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
S. Schäffler.
Unconstrained global optimization using stochastic integral
equations.
Optimization, 35(1):43-60, 1995.
S. Schäffler, R. Schultz, and K. Weinzierl.
Stochastic method for the solution of unconstrained vector
optimization problems.
J. Optim. Theory Appl., 114(1):209-222, 2002.
Stefan Schäffler.
Large-scale global optimization on transputer networks.
In Applied mathematics and parallel computing, pages 275-290.
Physica, Heidelberg, 1996.
I.P. Schagen.
The use of stochastic processes in interpolation and approximation.
Internat. J. Comput. Math., 8(1):63-76, 1980.
I.P. Schagen.
Sequential exploration of unknown multidimensional functions as an
aid to optimization.
IMA J. Numer. Anal., 4(3):337-347, 1984.
Manfred Schäl.
Existence of optimal policies in stochastic dynamic programming.
In Proceedings of the Sixth Conference on Probability Theory
(Braçsov, 1979), pages 205-219, Bucharest, 1981. Ed. Acad. R.S.
România.
Jack Schechtman.
An income fluctuation problem.
J. Econ. Theory 12, 218-241, 1976.
V. Scheiber.
Versuchsplaene zur stochastischen Optimierung.
Computing 9, 383-399, 1972.
Klaus Reiner Schenk-Hoppé.
Random dynamical systems in economics.
Stoch. Dyn., 1(1):63-83, 2001.
Kenneth Schilling.
Random knapsacks with many constraints.
Discrete Appl. Math. 48, No.2, 163-174, 1994.
Kenneth E. Schilling.
The growth of m-constraint random knapsacks.
Eur. J. Oper. Res. 46, No.1, 109-112, 1990.
L. Schmetterer.
über ein rekursives Verfahren von Herrn Hiriart-Urruty.
Österreich. Akad. Wiss. Math.-Natur. Kl. Sitzungsber. II,
189(4-7):139-147, 1980.
Leopold Schmetterer.
From stochastic approximation to the stochastic theory of
optimization.
Ber. Math.-Statist. Sekt. Forsch. Graz, 124-132:Ber. No. 127,
40, 1979.
Eleventh Styrian Mathematical Symposium (Graz, 1979).
Leopold Schmetterer.
Ueber convexe Programme mit stochastischen Stoerungen. (On convex
programs with stochastic perturbations).
Nova Acta Leopold., Neue Folge 61, No.267, 85-88, 1989.
R. Schmidt.
Optimale Wertpapiermischungen mit Hilfe des Chance-Constrained
Programming.
In Proc. Oper. Res. 4, DGOR Ann. Meet., Wuerzburg 1974,
51-71, 1974.
Lothar M. Schmitt.
Theory of genetic algorithms.
Theoret. Comput. Sci., 259(1-2):1-61, 2001.
S. Schmuntzsch.
Beruecksichtigung der Ertragsunsicherheit in Optimierungsmodellen
der Pflanzenproduktion.
Biometr. Z. 15, 301-312, 1973.
A.H. Schneeweiss.
Entscheidungskriterien bei Risiko.
Springer, Berlin, 1967.
(in German).
Ch. Schneeweiss.
Ueber den Zusammenhang von quadratischer stochastischer dynamischer
Programmierung und Wiener-Newton Theorie.
In Operations Res.-Verf. 13, IV. Oberwolfach-Tag. Operations
Res. 1971, 367- 379, 1972.
Ch. Schneeweiss.
Zur Theilschen Theorie dynamischer Sicherheitsaequivalente.
In Proc. Oper. Res., DGU Ann. Meet. 1971, 177-188, 1972.
Christoph Schneeweiss.
Optimale Prognosen und suffiziente Statistiken in quadratischen
dynamischen Optimierungsproblemen.
Statist. Hefte, n. F. 13, 116-129, 1972.
Christoph Schneeweiss.
Elemente einer Theorie hierarchischer Planung.
OR Spektrum 16, No.2, 161-168, 1994.
Wolfgang Schneiderheinze.
Numerically stable solution of linear least squares problems.
In Numerical methods of nonlinear programming and their
implementations (Quedlinburg, 1989), volume 60 of Math. Res., pages
127-144. Akademie-Verlag, Berlin, 1991.
Fabio Schoen.
Stochastic techniques for global optimization: a survey of recent
advances.
J. Global Optim., 1(3):207-228, 1991.
Fabio Schoen.
On a new stochastic global optimization algorithm based on censored
observations.
J. Global Optim., 4(1):17-35, 1994.
R. Schultz.
Rates of convergence in stochastic programs with complete integer
recourse.
Report 483, Schwerpunktprogramm der Deutschen Forschungsgemeinschaft
`Anwendungsbezogene Optimierung und Steuerung', 1993.
R. Schultz.
Discontinuous optimization problems in stochastic integer
programming.
Preprint SC 95-20, Konrad-Zuse-Zentrum für Informationstechnik
Berlin, 1995.
R. Schultz.
On structure and stability in stochastic programs with random
technology matrix and complete integer recourse.
Mathematical Programming, 70:73-89, 1995.
R. Schultz.
Rates of convergence in stochastic programs with complete integer
recourse.
SIAM Journal on Optimization, 6(4):1138-1152, 1996.
R. Schultz, L. Stougie, and M.H. van der Vlerk.
Two-stage stochastic integer programming: a survey.
Statist. Neerlandica, 50(3):404-416, 1996.
Rüdiger Schultz.
Some consequences of optimal-value-sensitivity for sensitivity of
optimal solutions in stochastic linear programming with recourse.
In 17. Jahrestagung "Mathematische Optimierung" (Rostock,
1986), volume 85 of Seminarberichte, pages 118-127. Humboldt Univ.
Berlin, 1986.
Rüdiger Schultz.
Continuity properties of expectation functions in stochastic integer
programming.
Math. Oper. Res., 18(3):578-589, 1993.
Rüdiger Schultz.
Strong convexity in stochastic programs with complete recourse.
J. Comput. Appl. Math., 56(1-2):3-22, 1994.
Stochastic programming: stability, numerical methods and applications
(Gosen, 1992).
Rüdiger Schultz.
On structure and stability in stochastic programs with random
technology matrix and complete integer recourse.
Math. Programming, 70(1, Ser. A):73-89, 1995.
Rüdiger Schultz.
A note on preprocessing via Fourier-Motzkin elimination in
two-stage stochastic programming.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 208-222. Springer, Berlin, 1998.
Rüdiger Schultz.
Some aspects of stability in stochastic programming.
Ann. Oper Res., 100:55-84 (2001), 2000.
Research in stochastic programming (Vancouver, BC, 1998).
Rüdiger Schultz, Leen Stougie, and Maarten H. van der Vlerk.
Solving stochastic programs with integer recourse by enumeration: a
framework using Gröbner basis reductions.
Math. Programming, 83(2, Ser. A):229-252, 1998.
Rüdiger Schultz and Stephan Tiedemann.
Risk aversion via excess probabilities in stochastic programs with
mixed-integer recourse.
Stochastic Programming E-Print Series, http://www.speps.org,
2002.
Rüdiger Schultz and Stephan Tiedemann.
Conditional value-at-risk in stochastic programs with mixed-integer
recourse.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
Rüdiger Schultz and Stephan Tiedemann.
Conditional value-at-risk in stochastic programs with mixed-integer
recourse.
Math. Program., 105(2-3, Ser. B):365-386, 2006.
Ruediger Schultz.
Continuity and stability in two-stage stochastic integer
programming.
In Stochastic optimization. Numerical methods and technical
applications, Proc. GAMM/IFIP-Workshop, Neubiberg/Ger. 1990, Lect. Notes
Econ. Math. Syst. 379, 81-92, 1992.
Andreas S. Schulz.
New Old Algorithms for Stochastic Scheduling.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Peter Schütz, Leen Stougie, and Asgeir Tomasgard.
Facility location with uncertain demand and economies of scale.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Gideon Schwarz.
Game theory and statistics.
In Handbook of game theory with economic applications, Vol.\
II, volume 11 of Handbooks in Econom., pages 769-779. North-Holland,
Amsterdam, 1994.
Eithan Schweitzer.
An interior random vector algorithm for multistage stochastic linear
programs.
SIAM J. Optim., 8(4):956-972 (electronic), 1998.
Eithan Schweitzer and Mordecai Avriel.
A Gaussian upper bound for Gaussian multi-stage stochastic linear
programs.
Math. Programming (Ser. A), 77(1):1-21, 1997.
Rudolf Scitovski.
An approach in solving nonlinear least squares problems.
In IV conference on applied mathematics (Split, 1984), pages
109-113. Univ. Split, Split, 1985.
C.H. Scott and T.R. Jefferson.
Convex stochastic programmes with simple recourse.
Int. J. Systems Sci. 10, 1143-1148, 1979.
C.H. Scott and T.R. Jefferson.
Zero degree of difficulty programs and the distribution problem.
Int. J. Syst. Sci. 11, 129-138, 1980.
T. J. Scott and E. G. Read.
Modelling hydro reservoir operation in a deregulated electricity
sector.
International Transactions in Operations Research,
3(3-4):209-221, 1996.
D. Sculli.
A stochastic cutting stock procedure: Cutting rolls of insulating
tape.
Manage. Sci. 27, 946-952, 1981.
Hans-Juergen Sebastian.
Dynamische Optimierung unter Einbeziehung zeitabhaengiger
Einflussparameter.
Math. Operationsforsch. Statistik 7, 427-452, 1976.
Hans-Juergen Sebastian.
Eine erweiterte Bellmannsche Funktionalgleichung der dynamischen
Optimierung.
Math. Operationsforsch. Stat., Optimization 8, 509-527, 1977.
Giovanni Sebastiani and Giovanni Luca Torrisi.
An extended ant colony algorithm and its convergence analysis.
Methodol. Comput. Appl. Probab., 7(2):249-263, 2005.
E.V. Sedunov.
Numerical methods for optimal unbiased design of an experiment in a
Hilbert space.
Kibernetika (Kiev), 5:55-62, 134, 1988.
R.S. Segall.
Some deterministic and stochastic nonlinear optimization modelling
for the spatial allocation of multicategorical resources: with an application
to real health data.
Appl. Math. Modelling 13, No.11, 641-650, 1989.
W. Seidel.
An open optimization problem in statistical quality control.
J. Global Optim., 1(3):295-303, 1991.
Abraham Seidmann.
Optimal dynamic routing in flexible manufacturing systems with
limited buffers.
Ann. Oper. Res. 15, 291-311, 1988.
M.P. Semesenko.
Random processes in control systems. (Sluchajnye protsessy v
sistemakh upravleniya).
Kiev-Donetsk: Vishcha Shkola. 192 p. R. 1.60, 1986.
S. Sen, R.D. Doverspike, and S. Cosares.
Network planning with random demand.
Telecommunication Systems 3:11-30, 1994.
Suvrajeet Sen.
Relaxations for probabilistically constrained programs with discrete
random variables.
In System modelling and optimization (Zurich, 1991), volume 180
of Lecture Notes in Control and Inform. Sci., pages 598-607. Springer,
Berlin, 1992.
Suvrajeet Sen.
Relaxations for probabilistically constrained programs with discrete
random variables.
Oper. Res. Lett., 11(2):81-86, 1992.
Suvrajeet Sen.
Subgradient decomposition and differentiability of the recourse
function of a two-stage stochastic linear program.
Oper. Res. Lett., 13(3):143-148, 1993.
Suvrajeet Sen and Julia L. Higle.
An Introductory Tutorial on Stochastic Linear Programming
Models.
Interfaces, 29(2):33-61, 1999.
Suvrajeet Sen and Julia L. Higle.
The c3 theorem and a d2 algorithm for large scale stochastic
integer programming: Set convexification.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Suvrajeet Sen and Julia L. Higle.
The C\sp 3 theorem and a D\sp 2 algorithm for large
scale stochastic mixed-integer programming: set convexification.
Math. Program., 104(1, Ser. A):1-20, 2005.
Suvrajeet Sen, Julia L. Higle, and John R. Birge.
Duality gaps in stochastic integer programming.
J. Global Optim., 18(2):189-194, 2000.
Suvrajeet Sen, Jason Mai, and Julia L. Higle.
Solution of large scale stochastic programs with stochastic
decomposition algorithms.
In Large scale optimization (Gainesville, FL, 1993), pages
388-410. Kluwer Acad. Publ., Dordrecht, 1994.
Suvrajeet Sen, Lihua Yu, and Talat Genc.
A stochastic programming approach to power portfolio optimization.
Oper. Res., 54(1):55-72, 2006.
Jati K. Sengupta.
Chance-constrained linear programming with chi-square type deviates.
Management Sci., 19(3):337-349, 1972.
Jati K. Sengupta.
Stochastic programming.
North-Holland Publishing Co., Amsterdam, 1972.
Methods and applications.
Jati K. Sengupta.
An iterative convex simplex method for geometric programming with
applications.
Internat. J. Systems Sci., 8(1):49-63, 1977.
Jati K. Sengupta.
Adaptive decision rules for stochastic linear programming.
Internat. J. Systems Sci., 9(1):97-109, 1978.
Jati K. Sengupta.
Bilinear models in stochastic programming.
J. Cybernet., 9(2):161-168, 1979.
Jati K. Sengupta.
A class of bilinear models in stochastic programming with
applications.
Internat. J. Systems Sci., 10(3):307-320, 1979.
Jati K. Sengupta.
Complementarity theorems in stochastic programming.
Internat. J. Systems Sci., 10(10):1081-1095, 1979.
Jati K. Sengupta.
Stochastic goal programming with estimated parameters.
Z. Nationalökonom., 39(3-4):225-243, 1979.
Jati K. Sengupta.
Testing and validation problems in stochastic linear programming.
J. Cybernet., 9(1):17-42, 1979.
Jati K. Sengupta.
Linear allocation rules under uncertainty.
Int. J. Syst. Sci. 11, 1459-1480, 1980.
Jati K. Sengupta.
Minimax solutions in stochastic programming.
Cybernet. Systems, 11(1-2):1-19, 1980.
Jati K. Sengupta.
Selecting an optimal solution in stochastic linear programming.
Internat. J. Systems Sci., 11(1):33-47, 1980.
Jati K. Sengupta.
Stochastic programming: a selective survey of recent economic
applications.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 525-536. Academic Press, London, 1980.
Jati K. Sengupta.
Stochastic programs as non-zero sum games.
Internat. J. Systems Sci., 11(10):1145-1162, 1980.
Jati K. Sengupta.
Decision models in stochastic programming, volume 7 of
North-Holland Series in System Science and Engineering.
North-Holland Publishing Co., New York, 1981.
Operational methods of decision making under uncertainty.
Jati K. Sengupta.
Optimal decisions under uncertainty, volume 193 of Lecture
Notes in Economics and Mathematical Systems.
Springer-Verlag, Berlin, 1981.
Jati K. Sengupta.
A minimax policy for optimal portfolio choice.
Int. J. Syst. Sci. 13, 39-56, 1982.
Jati K. Sengupta.
The active approach of stochastic optimization with new
applications.
In Econometrics of planning and efficiency, Vol. dedic. Mem. G.
Tintner, Adv. Stud. Theor. Appl. Econ. 11, 93-108, 1988.
Jati K. Sengupta.
Nonparametric tests of efficiency of portfolio investment.
Z. Nationalökonom., 50(1):1-15, 1989.
Jati K. Sengupta.
Robust solutions in stochastic linear programming.
J. Oper. Res. Soc. 42, No.10, 857-870, 1991.
Jati K. Sengupta.
The influence curve approach in data envelopment analysis.
Math. Program., Ser. B 52, No.1, 147-166, 1991.
Jati K. Sengupta.
Nonparametric approach to stochastic linear programming.
Internat. J. Systems Sci., 24(5):857-871, 1993.
Jati K. Sengupta and Raymond E. Sfeir.
Minimax method of measuring productive efficiency.
Int. J. Syst. Sci. 19, No.6, 889-904, 1988.
J.K. Sengupta.
A generalization of some distribution aspects of chance-constrained
linear programming.
Int. Econ. Rev. 11, 287-304, 1970.
J.K. Sengupta.
Stochastic linear programming with chance constraints.
Int. Econ. Rev. 11, 101-116, 1970.
J.K. Sengupta.
A system reliability approach to linear programming.
Unternehmensforsch. 15, 112-129, 1971.
J.K. Sengupta.
A statistical reliability approach to linear programming.
Unternehmensforschung, 15:255-278, 1971.
J.K. Sengupta.
Fractile programming under extreme value distributions for the
stochastic objective function.
J. Cybernet., 2(2):48-60, 1972.
J.K. Sengupta.
Application of system reliability measures in chance-constrained
programming.
Cahiers Centre Etud. Rech. oper. 15, 449-473, 1973.
J.K. Sengupta.
Estimating parameters of a linear programming model.
J. Cybernet., 6(3-4):309-328, 1976.
J.K. Sengupta.
Adaptive stochastic programming models in economic systems.
In Proceedings of the First International Conference on
Mathematical Modeling (St. Louis, Mo., 1977), Vol. V, pages 196-206, Rolla,
Mo., 1977. Univ. Missouri-Rolla.
J.K. Sengupta and G. Tintner.
A review of stochastic linear programming.
Rev. Inst. Int. Stat. 39, 197-223, 1971.
Sailes K. Sengupta.
Lower semicontinuous stochastic games with imperfect information.
Ann. of Statist. 3, 554-558, 1975.
Linn I. Sennott.
A new condition for the existence of optimal stationary policies in
average cost Markov decision processes.
Oper. Res. Lett. 5, 17-23, 1986.
Linn I. Sennott.
Computing average optimal constrained policies in stochastic dynamic
programming.
Probab. Engrg. Inform. Sci., 15(1):103-133, 2001.
Y. Seppaelae.
A stochastic multigoal investment model for the public sector.
In Prog. Oper. Res., Eger 1974, Colloq. Math. Soc. Janos Bolyai
12, 845-863 , 1976.
Yrjoe Seppaelae.
On accurate linear approximations for chance-constrained
programming.
J. Oper. Res. Soc. 39, No.7, 693-694, 1988.
Yrjoe Seppaelae and Tuomo Orpana.
Experimental study on the efficiency and accuracy of a
chance-constrained programming algorithm.
Eur. J. Oper. Res. 16, 345-357, 1984.
Yrjö Seppälä.
Constructing sets of uniformly tighter linear approximations for a
chance constraint.
Management Sci., 17:736-749, 1970/71.
Yrjö Seppälä.
A chance-constrained programming algorithm.
Nordisk Tidskr. Informationsbehandling (BIT), 12:376-399,
1972.
I.V. Sergienko and V.P. Shilo.
Probabilistic decomposition of integer linear programming problems
with Boolean variables, and the automatic choice of algorithms for their
solution.
Kibernet. Sistem. Anal., 2:149-158, 191, 1994.
G.N. Serikov.
A differential game with an integral plateau.
Differential Equations 1 994-999 (1976)., 1974.
S. P. Sethi, H. Yan, and H. Zhang.
Peeling layers of an onion: inventory model with multiple delivery
modes and forecast updates.
J. Optim. Theory Appl., 108(2):253-281, 2001.
S.P. Sethi and C. Bes.
Dynamic stochastic optimization problems in the framework of forecast
and decision horizons.
In Advances in optimization and control (Montreal, PQ, 1986),
volume 302 of Lecture Notes in Econom. and Math. Systems, pages
230-246. Springer, Berlin, 1988.
S.P. Sethi, W. Suo, M.I. Taksar, and Q. Zhang.
Optimal production planning in a stochastic manufacturing system with
long-run average cost.
J. Optim. Theory Appl., 92(1):161-188, 1997.
Jiri Sgall.
Online Scheduling.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Eli Shamir.
Remarks on the stochastic travelling salesman.
In Random graphs, Vol. 2 (Pozna\'n, 1989), Wiley-Intersci.
Publ., pages 233-236. Wiley, New York, 1992.
Ron Shamir.
Probabilistic analysis in linear programming.
In Probability and algorithms, pages 131-148. Nat. Acad.
Press, Washington, DC, 1992.
A. Shapiro.
Stochastic programming with equilibrium constraints.
J. Optim. Theory Appl., 128(1):223-243, 2006.
A. Shapiro and Y. Wardi.
Convergence analysis of gradient descent stochastic algorithms.
J. Optim. Theory Appl., 91(2):439-454, 1996.
A. Shapiro and Y. Wardi.
Convergence analysis of stochastic algorithms.
Math. Oper. Res., 21(3):615-628, 1996.
Alexander Shapiro.
Asymptotic properties of statistical estimators in stochastic
programming.
Ann. Statist., 17(2):841-858, 1989.
Alexander Shapiro.
On differential stability in stochastic programming.
Math. Programming, 47(1 (Ser. A)):107-116, 1990.
Alexander Shapiro.
Asymptotic analysis of stochastic programs.
Ann. Oper. Res., 30(1-4):169-186, 1991.
Stochastic programming, Part I (Ann Arbor, MI, 1989).
Alexander Shapiro.
Asymptotic behavior of optimal solutions in stochastic programming.
Math. Oper. Res., 18(4):829-845, 1993.
Alexander Shapiro.
Quantitative stability in stochastic programming.
Math. Programming, 67(1, Ser. A):99-108, 1994.
Alexander Shapiro.
Simulation-based optimization-convergence analysis and statistical
inference.
Comm. Statist. Stochastic Models, 12(3):425-454, 1996.
Alexander Shapiro.
Statistical inference of stochastic optimization problems.
In Probabilistic constrained optimization, volume 49 of
Nonconvex Optim. Appl., pages 282-307. Kluwer Acad. Publ., Dordrecht, 2000.
Alexander Shapiro.
Stochastic programming by monte carlo simulation methods.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Alexander Shapiro.
Statistical inference of multistage stochastic programming problems.
Optimization Online, http://www.optimization-online.org, 2002.
Alexander Shapiro.
On complexity of multistage stochastic programs.
Oper. Res. Lett., 34(1):1-8, 2006.
Alexander Shapiro.
Stochastic programming approach to optimization under uncertainty.
Optimization Online, http://www.optimization-online.org, 2006.
Alexander Shapiro.
Worst-case distribution analysis of stochastic programs.
Math. Program., 107(1-2, Ser. B):91-96, 2006.
Alexander Shapiro and Shabbir Ahmed.
On a class of minimax stochastic programs.
SIAM J. Optim., 14(4):1237-1249 (electronic), 2004.
Alexander Shapiro and Tito Homem-de Mello.
A simulation-based approach to two-stage stochastic programming with
recourse.
Math. Programming, 81(3, Ser. A):301-325, 1998.
Alexander Shapiro and Tito Homem-de-Mello.
On rate of convergence of optimal solutions of Monte Carlo
approximations of stochastic programs.
Stochastic Programming E-Print Series, http://www.speps.org,
1999.
Alexander Shapiro and Tito Homem-de Mello.
On the rate of convergence of optimal solutions of Monte Carlo
approximations of stochastic programs.
SIAM J. Optim., 11(1):70-86 (electronic), 2000.
Alexander Shapiro, Tito Homem-de Mello, and Joocheol Kim.
Conditioning of convex piecewise linear stochastic programs.
Math. Program., 94(1, Ser. A):1-19, 2002.
Alexander Shapiro, Joocheol Kim, and Tito Homem-de-Mello.
Conditioning of stochastic programs.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Alexander Shapiro and Anton Kleywegt.
Minimax analysis of stochastic problems.
Optim. Methods Softw., 17(3):523-542, 2002.
Stochastic programming.
Alexander Shapiro and Arkadi Nemirovski.
On complexity of stochastic programming problems.
In Continuous optimization, volume 99 of Appl. Optim.,
pages 111-146. Springer, New York, 2005.
Alexander Shapiro and Huifu Xu.
Uniform laws of large numbers for set-valued mappings and
subdifferentials of random functions.
Optimization Online, http://www.optimization-online.org, 2005.
John J. Shaw, Ronald M. James, and Daniel B. Grunberg.
Birth of a salesman.
J. Guid. Control Dyn. 11, No.5, 415-420, 1988.
Yosef Sheffi and Warren B. Powell.
Equivalent minimization programs and solution algorithms for
stochastic equilibrium transportation network problems.
In Combinatorics, graph theory and computing, Proc. 11th
southeast. Conf., Boca Raton/Florida 1980, Vol. I, Congr. Numerantium 28,
81-115, 1980.
Yosef Sheffi and Warren B. Powell.
An algorithm for the equilibrium assignment problem with random link
times.
Networks 12, 191-207, 1982.
Ronald W. Shephard, Rokaya A. Al-Ayat, and Robert C. Leachman.
Shipbuilding production function: An example of a dynamic production
function.
In Quant. Wirtsch.-Forsch., W. Krelle zum 60. Geb., 627-654,
1977.
Hanif D. Sherali and Barbara M. P. Fraticelli.
A modification of Benders' decomposition algorithm for discrete
subproblems: an approach for stochastic programs with integer recourse.
J. Global Optim., 22(1-4):319-342, 2002.
Dedicated to Professor Reiner Horst on his 60th birthday.
H.D. Sherali, A.L. Soyster, F.H. Murphy, and S. Sen.
Allocation of capital costs in electric utility capacity expansion
planning under uncertainty.
Management Science 30:1-19, 1984.
V. R. Sherkat, R. Campo, K. Moslehi, and E. O. Lo.
Stochastic long-term hydrothermal optimization for a multireservoir
system.
IEEE Trans. Power Apparatus and Systems, 104(8):2040-2049,
1985.
Theodore J. Sheskin.
Sequencing of diagnostic tests for fault isolation by dynamic
programming.
IEEE Trans. Reliab. R-27, 353-359, 1978.
Ding-hua Shi and Jian-ping Peng.
A new theoretical framework for analyzing stochastic global
optimization algorithms.
J. Shanghai Univ., 3(3):175-180, 1999.
Hong Yan Shi, Zhi Fei Chen, and Chang Zhi Sun.
On the convergence of a hybrid optimization algorithm.
Control Decis., 19(5):546-549, 553, 2004.
Leyuan Shi and Sigurdur Ólafsson.
Nested partitions method for stochastic optimization.
Methodol. Comput. Appl. Probab., 2(3):271-291, 2000.
Leyuan Shi and Sigurdur Ólafsson.
Stopping rules for the stochastic nested partitions method.
Methodol. Comput. Appl. Probab., 2(1):37-58, 2000.
Xiao Fa Shi and Jin De Wang.
Approximate Lagrange multiplier algorithm for stochastic programs
with complete recourse: nonlinear deterministic constraints.
Acta Math. Appl. Sinica (English Ser.), 8(3):207-213, 1992.
S.V. Shibaev.
A stochastic algorithm for finding a maximin.
In Mathematical methods for the analysis of complex stochastic
systems (Russian), pages 12-17, i-ii. Akad. Nauk Ukrain. SSR Inst.
Kibernet., Kiev, 1988.
S.V. Shibaev.
A method for finding the minimax of functionals that depend on
probability measures.
Kibernet. Sistem. Anal., 2:79-85, 189, 1992.
S.V. Shibaev.
On finding maximin under marginal constraints.
Kibernet. Sistem. Anal., 6:103-114, 186, 1994.
Wei Shih.
Joint determination of pricings, productions and budget allocations
with a chance-constrained approach.
Int. J. Inf. Manage. Sci. 5, No.2, 41-52, 1994.
T. Shiina.
Numerical solution technique for joint chance-constrained programming
problem -an application to electric power capacity expansion-.
Journal of the Operations Research Society of Japan,
42:128-140, 1999.
T. Shiina.
L-shaped decomposition method for multi-stage stochastic
concentrator location problem.
Journal of the Operations Research Society of Japan,
43:317-332, 2000.
Takayuki Shiina.
L-shaped method for stochastic integer programming problem.
S¯urikaisekikenky¯usho K¯oky¯uroku, (1132):154-164, 2000.
Mathematical decision theory under uncertainty and ambiguity
(Japanese) (Kyoto, 1999).
Takayuki Shiina and John R. Birge.
Stochastic unit commitment problem.
S¯urikaisekikenky¯usho K¯oky¯uroku, (1252):117-123, 2002.
Mathematical decision making under uncertainty (Japanese) (Kyoto,
2001).
Takayuki Shiina and John R. Birge.
Stochastic unit commitment problem.
Int. Trans. Oper. Res., 11(1):19-32, 2004.
V. A. Shikhovtsev.
The recursive minimization method in problems of the optimization of
dynamical systems under uncertainty.
Izv. Akad. Nauk Teor. Sist. Upr., (1):40-57, 1999.
S.V. Shil'man.
Stochastic approximation in filtering and identification problems.
Eng. Cybern. 21, No.4, 77-84 translation from Izv. Akad. Nauk
SSSR, Tekh. Kibern. 1983, No.4, 86-94 (1983)., 1983.
S.V. Shil'man.
A stochastic quasigradient method for quadratic optimization under
dependent observations.
Avtomat. i Telemekh., 12:70-80, 1992.
S.V. Shil'man and A.I. Yastrebov.
Stochastic optimization algorithms with Markov noise in gradient
measurements.
Autom. Remote Control 41, 817-821 translation from Avtom.
Telemekh. 1980, No.6, 96-100 (1980)., 1980.
V. P. Shilo.
A method of global equilibrium search.
Kibernet. Sistem. Anal., (1):74-81, 190, 1999.
V. P. Shilo.
RESTART distributions and an asymptotically exact random
algorithm for solving discrete optimization problems.
Dopov. Nats. Akad. Nauk Ukr. Mat. Prirodozn. Tekh. Nauki,
(2):85-88, 2001.
V. P. Shilo.
Sorting algorithms for solving discrete optimization problems and
RESTART distributions.
Dopov. Nats. Akad. Nauk Ukr. Mat. Prirodozn. Tekh. Nauki,
(1):79-83, 2001.
V.P. Shilo.
Application of stochastic programming methods in diffusion process
control problems.
Cybernetics 14, 741-745 translation from Kibernetika 1978, No.5,
92-95 (1978)., 1979.
V.P. Shilo.
Application of stochastic programming methods in problems of
optimizing systems described by elliptic equations.
In Probabilistic methods in cybernetics, volume 69 of
Preprint 79, pages 12-20, 73. Akad. Nauk Ukrain. SSR Inst. Kibernet., Kiev,
1979.
Takashi Shima.
Global optimization by generalized random tunneling algorithm and its
application to system identification and control problems using neural
networks.
Trans. Inst. Systems Control Inform. Engrs., 7(3):84-93, 1994.
T. Shimizu.
A stochastic approximation method for optimization problems.
J. Assoc. Comput. Mach. 16, 511-516, 1969.
Nahum Shimkin.
Extremal large deviations in controlled i.i.d. processes with
applications to hypothesis testing.
Adv. in Appl. Probab., 25(4):875-894, 1993.
S. Shiode, H. Ishii, and T. Nishida.
Errata: "A stochastic linear knapsack problem" [Naval Res.\
Logist. 34 (1987), no. 5, 753-759; MR 88h:90158].
Naval Res. Logist., 37(4):600, 1990.
Shogo Shiode and Hiroaki Ishii.
A single facility stochastic location problem under A-distance.
Ann. Oper. Res. 31, 469-478, 1991.
Sh¯ogo Shiode, Hiroaki Ishii, and Toshio Nishida.
A chance constrained minimax facility location problem.
Math. Japon., 30(5):783-803, 1985.
Sh¯ogo Shiode, Hiroaki Ishii, and Toshio Nishida.
A stochastic linear knapsack problem.
Naval Res. Logist., 34(5):753-759, 1987.
A.V. Shirin.
On an algorithm for the estimation of parameters of linear
regression equations by the method of random improving.
Probl. Sluchajnogo Poiska 8, 275-279, 1980.
David B. Shmoys and Chaitanya Swamy.
An approximation scheme for stochastic linear programming and its
application to stochastic integer programs.
J. ACM, 53(6):978-1012 (electronic), 2006.
E.I. Shor.
Optimal control of an object with a multiplicative continuously
distributed unknown parameter.
Kibernetika 1982, No.1, 121-122, 1982.
N. Z. Shor, T. A. Bardadym, N. G. Zhurbenko, A. P. Likhovid, and P. I.
Stetsyuk.
Application of nonsmooth optimization methods in stochastic
programming problems.
Kibernet. Sistem. Anal., (5):33-47, 188, 1999.
Eugene Shragowitz and Rung-Bin Lin.
Combinatorial optimization, Markov chains, and stochastic automata.
In Numerical solution of Markov chains, volume 8 of
Probab. Pure Appl., pages 543-564. Dekker, New York, 1991.
A.K. Shukla and S.N. Gupta.
A stochastic linear programming approach for crop planning.
Acta Cienc. Indica, Math. 15, No.3, 265-270, 1989.
A.K. Shukla and S.N. Gupta.
A stochastic model for minimizing variability.
Acta Cienc. Indica, Math. 15, No.3, 259-264, 1989.
Andrzej Sieroci\'nski and Jerzy Zabczyk.
On a packing problem.
In Stochastic systems and optimization (Warsaw, 1988), volume
136 of Lecture Notes in Control and Inform. Sci., pages 356-359.
Springer, Berlin, 1989.
C.E. Sigal, A.A.B. Pritsker, and J.J. Solberg.
The use of cutsets in Monte Carlo analysis of stochastic
networks.
Math. Comput. Simulation, 21(4):376-384, 1979.
C.Elliott Sigal, A.Alan B. Pritsker, and James J. Solberg.
The stochastic shortest route problem.
Oper. Res. 28, 1122-1129, 1980.
Wojciech Sikora.
Transportation problem with random demand.
Przegl. Stat. 39, No.3-4, 351-364, 1992.
K.A. Sikorski and A. Borkowski.
Ultimate load analysis by stochastic programming.
In Smith, D. Lloyd (ed.): Mathematical programming methods in
structural plasticity. Wien etc.: Springer-Verlag (ISBN 3-211-82191-0). CISM
Courses Lect. 299, 403-424, 1990.
V.P. Silo.
Elliptic equations and boundary control.
In Methods in operations research and reliability theory in
systems of analysis (Russian), pages 76-80, 102, Kiev, 1979. Akad. Nauk
Ukrain. SSR Inst. Kibernet.
Eduardo F. Silva and R. Kevin Wood.
Solving a class of stochastic mixed-integer programs with branch and
price.
Math. Program., 108(2-3, Ser. B):395-418, 2006.
Thomas M. Simundich.
An efficient algorithm for solving a stochastic, integer programming
problem arising in radio navigation.
In Optim. Techn., Proc. IFIP Conf., Wuerzburg 1977, Part 2,
Lect. Notes Control Inf. Sci. 7, 263-268, 1978.
Diptendu Sinha and Jerry C. Wei.
Stochastic analysis of flexible process choices.
Eur. J. Oper. Res. 60, No.2, 183-199, 1992.
Hans-Werner Sinn.
Economic decisions under uncertainty. Transl. from the German. 2nd
ed., 1989.
K. Sirlantzis, J. D. Lamb, and W. B. Liu.
Novel algorithms for noisy minimization problems with applications to
neural networks training.
J. Optim. Theory Appl., 129(2):325-340, 2006.
Karel Sladký.
On a multistage stochastic linear program.
In Asymptotic statistics (Prague, 1993), Contrib. Statist.,
pages 435-446. Physica, Heidelberg, 1994.
R. Slowi\'nski and J. Teghem.
A comparison study of "STRANGE" and "FLIP".
In Stochastic versus fuzzy approaches to multiobjective
mathematical programming under uncertainty, volume 6 of Theory Decis.
Lib. Ser. D System Theory Knowledge Engrg. Probl. Solving, pages 365-393.
Kluwer Acad. Publ., Dordrecht, 1990.
Roman Slowinski.
A multicriteria fuzzy linear programming method for water supply
system development planning.
Fuzzy Sets Syst. 19, 217-237, 1986.
Roman Slowi\'nski and Jacques Teghem, editors.
Stochastic versus fuzzy approaches to multiobjective
mathematical programming under uncertainty, volume 6 of Theory and
Decision Library. Series D: System Theory, Knowledge Engineering and Problem
Solving.
Kluwer Academic Publishers Group, Dordrecht, 1990.
Y. Smeers.
Traitement de l'incertain dans le calcul des plans d'équipement
électrique.
In B. Cornet and H. Tulkens, editors, Modélisation et
Décision Economique, Bruxelles, 1990. De Boeck Université.
V.V. Smirnova.
Inequality systems with random coefficients in linear stochastic
programming.
Issled. Oper. ASU 20, 49-55, 1982.
V.V. Smirnova.
Systems of inequalities with random coefficients in linear stochastic
programming.
Issled. Operatsii i ASU, 20:49-55, 1982.
Douglas V. Smith.
Decision rules in chance-constrained programming: Some experimental
comparisons.
Management Sci., Appl. 19, 688-702, 1973.
J. Cole Smith, Andrew J. Schaefer, and Joyce W. Yen.
A stochastic intra-ring synchronous optimal network design problem.
Stochastic Programming E-Print Series, http://www.speps.org,
2002.
J. MacGregor Smith and Nikhil Chikhale.
Buffer allocation for a class of nonlinear stochastic knapsack
problems.
Ann. Oper. Res., 58:323-360, 1995.
Applied mathematical programming and modeling, II (APMOD 93)
(Budapest, 1993).
James E. Smith and Kevin F. McCardle.
Structural properties of stochastic dynamic programs.
Oper. Res., 50(5):796-809, 2002.
L.G. Smyshlyaeva.
Sufficient conditions for optimizing the dimension of the state
vector of a dynamical system.
Vestnik Leningrad. Univ. Mat. Mekh. Astronom., 1:42-45, 123,
1989.
Moshe Sniedovich.
Preference order stochastic knapsack problems: Methodological
issues.
J. Oper. Res. Soc. 31, 1025-1032, 1980.
Moshe Sniedovich.
Analysis of a preference order traveling salesman problem.
Oper. Res. 29, 1234-1237, 1981.
Moshe Sniedovich.
Some comments on preference order dynamic programming models.
J. Math. Anal. Appl. 79, 489-501, 1981.
Moshe Sniedovich.
A class of variance-constrained problems.
Oper. Res., 31(2):338-353, 1983.
Juan-Miguel Soler Salcedo.
A model of cybernetic stochastic economy.
Revista Acad. Ci. Madrid 66, 11-16, 1972.
D. P. Solomatine.
Two strategies of adaptive cluster covering with descent and their
comparison to other algorithms.
J. Global Optim., 14(1):55-78, 1999.
J.E. Somers.
Maximum flow in networks with a small number of random arc
capacities.
Networks 12, 241-253, 1982.
Laszlo Somlyody and Roger J.-B. Wets.
Stochastic optimization models for lake eutrophication management.
Oper. Res. 36, No.5, 660-681, 1988.
J. Song, Y. Xu, Y. Yam, and M.C. Nechyba.
Optimization of human control strategy with simultaneous perturbation
stochastic approximation.
In Proceedings of the IEEE Conference on Intelligent Robots
and Systems, Part 2, pages 983-988, 1998.
N.Z. Sor and M.B. Scepakin.
An algorithm for solution of a two step problem of stochastic
programming.
Kibernetika (Kiev), 3:56-58, 1968.
N.Z. Sor and M.B. Scepakin.
Multistage stochastic programming problems in parametric form.
In Theory of optimal solutions (Proc. Sem., Kiev, 1968), No. 3
(Russian), pages 64-73, Kiev, 1969. Akad. Nauk Ukrain. SSR.
Marilda de Oliveira Sotomayor.
On income fluctuations and capital gains with a convex production
function.
J. Econom. Dynamics Control, 11(3):285-312, 1987.
M. Souissi and Y. Smeers.
Reliability optimization of complex systems using sharp lower bounds.
In System modelling and optimization (Prague, 1995), pages
339-346. Chapman & Hall, London, 1996.
Charles R. Sox.
Dynamic lot sizing with random demand and non-stationary costs.
Oper. Res. Lett., 20(4):155-164, 1997.
A.L. Soyster, R.D. Foley, and F.H. Murphy.
A class of stochastic mathematical programs with correlated scale
parameters in the objective and right-hand side.
Oper. Res., 32(4):945-951, 1984.
R. Spaans and R. Luus.
Importance of search-domain reduction in random optimization.
J. Optim. Theory Appl., 75(3):635-638, 1992.
James C. Spall.
A one-measurement form of simultaneous perturbation stochastic
approximation.
Automatica J. IFAC, 33(1):109-112, 1997.
James C. Spall.
Stochastic optimization.
In Handbook of computational statistics, pages 169-197.
Springer, Berlin, 2004.
J.C. Spall.
A stochastic approximation technique for generating maximum
likelihood parameter estimates.
In Proceedings of the American Control Conference, pages
1161-1167, 1987.
J.C. Spall.
Multivariate stochastic approximation using a simultaneous
perturbation gradient approximation.
IEEE Transactions on Automatic Control, 37:332-341, 1992.
J.C. Spall.
Developments in stochastic optimization algorithms with gradient
approximations based on function measurements.
In Proceedings of the Winter Simulation Conference, eds. J.D.
Tew, M.S. Manivannan, D.A. Sadowski, and A.F. Seila, pages 207-214, 1994.
J.C. Spall.
Adaptive stochastic approximation by the simultaneous perturbation
method.
In Proceedings of the IEEE Conference on Decision and
Control, pages 3872-3879, 1998.
J.C. Spall.
Implementation of the simultaneous perturbation algorithm for
stochastic optimization.
IEEE Transactions on Aerospace and Electronic Systems,
34:817-823, 1998.
J.C. Spall.
Resampling-based calculation of the information matrix in nonlinear
statistical models.
In Proceedings of the 4th Joint Conference on Information
Sciences, volume 4, pages 35-39, Research Triangle Park, NC, October 1998.
J.C. Spall.
Adaptive stochastic approximation by the simultaneous perturbation
method.
IEEE Transactions on Automatic Control, 45:in press, 2000.
J.C. Spall and J.A. Cristion.
Nonlinear adaptive control using neural networks: Estimation based on
a smoothed form of simultaneous perturbation gradient approximation.
Statistica Sinica, 4:1-27, 1994.
J.C. Spall and J.A. Cristion.
A neural network controller for systems with unmodeled dynamics with
applications to wastewater treatment.
IEEE Transactions on Systems, Man, and Cybernetics?B,
27:369-375, 1997.
J.C. Spall and J.A. Cristion.
Model-free control of nonlinear stochastic systems with discrete-time
measurements.
IEEE Transactions on Automatic Control, 43:1198-1210, 1998.
J.C. Spall, S.D. Hill, and D.S. Stark.
Some theoretical comparisons of evolutionary computation and other
optimization approaches.
In Proceedings of the Congress on Evolutionary Computation,
pages 1398-1405, Washington, DC, July 1999.
Michael Spence and David Starrett.
Most rapid approach paths in accumulation problems.
Internat. econom. Review 16, 388-403, 1975.
Georghios P. Sphicas and Fred N. Silverman.
Deterministic equivalents for stochastic assembly line balancing.
AIIE Trans., 8(2):280-282, 1976.
Liliana Spircu.
Distribution of optimum in stochastic linear programming.
Econ. Comput. Econ. Cybern. Stud. Res. 1977, No.4, 81-92, 1977.
Wolfgang Stadje.
Goal-programming- und Nutzenmodelle für
Entscheidungssituationen mit mehreren Zielen und stochastischen
Informationen.
In Operations Research Verfahren, 30, pages 166-183. Hain,
Meisenheim, 1979.
Wolfgang Stadje.
Nutzen- und Goal-Programming-Modelle fuer das Vektormaximumproblem
und stochastische Verallgemeinerungen.
Oper. Res. Verfahren 34, 289-299, 1979.
Wolfgang Stadje.
Availability of an operating system during a given time interval: a
dynamic programming approach.
Naval Res. Logist., 43(4):589-602, 1996.
I. Stancu-Minasian.
The stochastic Chebyshev problem. The distribution function of the
optimum.
Studii Cerc. Mat. 30, 567-577, 1978.
I. M. Stancu-Minasian, R. Caballero, E. Cerdá, and M. M. Muñoz.
The stochastic bottleneck linear programming problem.
Top, 7(1):123-143, 1999.
I. M. Stancu-Minasian and Stefan Tigan.
Continuous time linear-fractional programming. The minimum-risk
approach.
RAIRO Oper. Res., 34(4):397-409, 2000.
I.M. Stancu-Minasian.
Note on stochastic programming with several objective functions
having the vector c random.
Stud. Cerc. Mat., 27(4):453-459, 1975.
I.M. Stancu-Minasian.
On the multiple minimum risk problem. I. The case of two
objective functions.
Stud. Cerc. Mat., 28(5):617-623, 1976.
I.M. Stancu-Minasian.
On the problem of Kataoka.
Stud. Cerc. Mat., 28(1):95-111, 1976.
I.M. Stancu-Minasian.
On the problem of minimum multiple risk. II. The case of r\ (r > 2) objective functions.
Stud. Cerc. Mat., 28(6):723-734, 1976.
I.M. Stancu-Minasian.
On stochastic programming with multiple objective functions.
In Proceedings of the Fifth Conference on Probability Theory
(Bra sov, 1974), pages 429-436. Editura Acad. R.S.R., Bucharest, 1977.
I.M. Stancu-Minasian.
On the multiple minimum risk problem.
Bull. Math. Soc. Sci. Math. R.S. Roumanie (N.S.),
23(71)(4):427-437, 1979.
I.M. Stancu-Minasian.
Programarea stocastica cu mai multe funçtii obiectiv.
Editura Academiei Republicii Socialiste România, Bucharest, 1980.
With a preface by Marius Iosifescu, With an English summary.
I.M. Stancu-Minasian.
Stochastic programming with multiple objective functions.
Mathematics and its Applications (East European Series). D. Reidel
Publishing Co., Dordrecht, 1984.
Translated from the Romanian by Victor Giurgiu tiu.
I.M. Stancu-Minasian.
Overview of different approaches for solving stochastic programming
problems with multiple objective functions.
In Stochastic versus fuzzy approaches to multiobjective
mathematical programming under uncertainty, volume 6 of Theory Decis.
Lib. Ser. D System Theory Knowledge Engrg. Probl. Solving, pages 71-101.
Kluwer Acad. Publ., Dordrecht, 1990.
I.M. Stancu-Minasian and S. Tigan.
In Komlosi, Sandor (ed.) et al., Proceedings of the 5th
international workshop on generalized convexity held at Janus Pannonius
University, Pecs, Hungary, August 31-September 2, 1992. Berlin:
Springer-Verlag, (ISBN 3-540-57624-X). Lect. Notes Econ. Math. Syst. 405,
322-333.
I.M. Stancu-Minasian and S. Tigan.
The stochastic linear-fractional max-min problem.
In Itinerant Seminar on Functional Equations, Approximation and
Convexity (Cluj-Napoca, 1987), volume 87 of Preprint, pages 275-280,
Cluj, 1987. Univ. "Babe s-Bolyai ".
I.M. Stancu-Minasian and S. Tigan.
On some fractional programming models occurring in minimum-risk
problems.
In Generalized convexity and fractional programming with
economic applications, Proc. Workshop, Pisa/Italy 1988, Lect. Notes Econ.
Math. Syst. 345, 295-324, 1990.
I.M. Stancu-Minasian and St. Tigan.
The vectorial minimum-risk problem.
In Proceedings of the colloquium on approximation and
optimization (Cluj-Napoca, 1985), pages 321-328, Cluj, 1985. Univ.
Cluj-Napoca.
I.M. Stancu-Minasian and St. Tigan.
A stochastic approach to some linear fractional goal programming
problems.
Kybernetika (Prague), 24(2):139-149, 1988.
I.M. Stancu-Minasian and St. Tigan.
Multiobjective mathematical programming with inexact data.
In Stochastic versus fuzzy approaches to multiobjective
mathematical programming under uncertainty, volume 6 of Theory Decis.
Lib. Ser. D System Theory Knowledge Engrg. Probl. Solving, pages 395-418.
Kluwer Acad. Publ., Dordrecht, 1990.
I.M. Stancu-Minasian and Stefan Tigan.
The minimum risk approach to special problems of mathematical
programming. The distribution function of the optimal value.
Anal. Numér. Théor. Approx., 13(2):175-187, 1984.
I.M. Stancu-Minasian and M.J. Wets.
A research bibliography in stochastic programming, 1955-1975.
Operations Res., 24(6):1078-1119, 1976.
Nikolai Stanulov and Ivailo Velev.
Information model for optimum operation planning of the production
for an industrial complex.
B"lgar. Akad. Nauk., Izv. Inst. tehn. Kibernetika 18, 49-59,
1974.
R.M. Stark.
On zero-degree stochastic geometric programs.
J. Optimization Theory Appl., 23(1):167-182, 1977.
Scott Stark.
Stochastic global optimization applied to reaction network parameter
estimation.
In Parallel processing for scientific computing (Houston, TX,
1991), pages 180-185. SIAM, Philadelphia, PA, 1992.
J. R. Stedinger.
Stochastic multi-reservoir hydroelectric scheduling.
In Proceedings 28. Internationales Wasserbau-Symposium,
Wasserwirtschafliche Systeme-Konzepte, Konflikte, Kompromisse, pages
89-117, Aachen, Germany, 1998. Institut für Wasserbau und
Wasserwirtschaft, Rheinisch-Westfälischen Technischen Hochschule Aachen.
J. Michael Steele.
Probability and problems in Euclidean combinatorial optimization.
In Probability and algorithms, pages 109-129. Nat. Acad.
Press, Washington, DC, 1992.
J.Michael Steele.
Probabilistic algorithm for the directed traveling salesman
problem.
Math. Oper. Res. 11, 343-350, 1986.
Stefan M. Stefanov.
On the implementation of stochastic quasigradient methods to some
facility location problems.
Yugosl. J. Oper. Res., 10(2):235-256, 2000.
Marc Steinbach.
Tree-Sparse Modeling and Solution of Multistage Stochastic
Programs.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Marc C. Steinbach.
Hierarchical sparsity in multistage stochastic programs.
In Stochastic optimization: algorithms and applications
(Gainesville, FL, 2000), pages 385-410. Kluwer Acad. Publ., Dordrecht,
2001.
Marc C. Steinbach.
Markowitz revisited: mean-variance models in financial portfolio
analysis.
SIAM Rev., 43(1):31-85 (electronic), 2001.
Marc C. Steinbach.
Tree-sparse convex programs.
Math. Methods Oper. Res., 56(3):347-376, 2002.
Karl-Heinz Stenvers.
Stochastische Vektoroptimierung zur Auslegung von
Tragwerksstrukturen - Loesungsalgorithmen und Versuchsergebnisse. (Stochastic
vector optimization for construction of load-bearing structures - Solution
algorithms and experimental results)., 1985.
V.E. Stepanov.
Phase transitions in random graphs.
Teor. Veroyatn. Primen. 15, 200-216, 1970.
L. Stettner and J. Zabczyk.
Stochastic version of a penalty method.
In Optimization techniques, Proc. 9th IFIP Conf., Warsaw 1979,
Part 1, Lect. Notes Control Inf. Sci. 22, 179-183, 1980.
William R.jun. Stewart and Bruce L. Golden.
Stochastic vehicle routing: A comprehensive approach.
Eur. J. Oper. Res. 14, 371-385, 1983.
Richard H. Stockbridge.
Portfolio optimization in markets having stochastic rates.
In Stochastic theory and control (Lawrence, KS, 2001), volume
280 of Lecture Notes in Control and Inform. Sci., pages 447-458.
Springer, Berlin, 2002.
Gerald Stöckl.
A stochastic linear programming approach in topology and material
optimization.
In Stochastic optimization techniques (Neubiberg/Munich, 2000),
volume 513 of Lecture Notes in Econom. and Math. Systems, pages
343-364. Springer, Berlin, 2002.
M. Stoica.
Stochastic splitors used in scheduling.
Econ. Comput. Econ. Cybern. Stud. Res. 1983, No.1, 63-71, 1983.
L.S. Stoikova and G.N. Sakovich.
Sharp upper bounds for a distribution function in a class of unimodal
distributions with fixed moments.
Dokl. Akad. Nauk Ukrain. SSR Ser. A, 87(1):29-32, 87, 1988.
L.S. Stojkova.
Prelimit distribution functions and limit polynomials in a Chebyshev
extremal problem.
Cybernetics 17, 824-835 translation from Kibernetika 1981, No.6,
95-104 (1981)., 1982.
L.S. Stojkova.
On conditions realizing the partitioning of the parameter domain in
a Chebyshev extremal problem.
Diskretn. Mat. 2, No.4, 11-17, 1990.
Longin Stolc.
Stochastic linear programming method for right-hand sides random
vector.
Internat. J. Systems Sci., 22(7):1197-1208, 1991.
Longin Stolc and Miroslaw Kwiesielewicz.
Methods of non-deterministic linear programming in multihorizon
system production control.
Adv. Modelling Simulation 20, No.3, 33-45, 1990.
L. Stougie.
Design and analysis of algorithms for stochastic integer
programming, volume 37 of CWI Tract.
Stichting Mathematisch Centrum Centrum voor Wiskunde en Informatica,
Amsterdam, 1987.
L. Stougie and M.H. van der Vlerk.
Stochastic integer programming.
In M. Dell'Amico, F. Maffioli, and S. Martello, editors,
Annotated Bibliographies in Combinatorial Optimization, chapter 9, pages
127-141. Wiley, 1997.
Leen Stougie and Maarten H. van der Vlerk.
Approximation in stochastic integer programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
B. Strazicky.
On an algorithm for solution of the two-stage stochastic programming
problem.
In Sechste Oberwolfach-Tagung über Operations Research (1973),
Teil II, pages 142-156. Operations Research Verfahren, Band XIX, Meisenheim
am Glan, 1974. Hain.
Beáta Strazicky.
On a method for solving the two-stage stochastic programming problem.
Közlemények-MTA Számitástechn. Automat. Kutató Int.
Budapest, 11:33-49, 1973.
Beáta Strazicky.
Some results concerning an algorithm for the discrete recourse
problem.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 263-274. Academic Press, London, 1980.
L. Streitferdt.
Zur Loesung einiger spezieller Verteilungsprobleme der
stochastischen Programmierung.
In Proc. Oper. Res. 2, DGOR Ann. Meet. Hamburg 1972, 363-377,
1973.
Simon Streltsov and Pirooz Vakili.
A non-myopic utility function for statistical global optimization
algorithms.
J. Global Optim., 14(3):283-298, 1999.
Peter A. Streufert.
Biconvergent stochastic dynamic programming, asymptotic impatience,
and "average" growth.
J. Econom. Dynam. Control, 20(1-3):385-413, 1996.
Z. Strezova.
An approach to the study of complex decision-making systems.
In Progr. Cybern. Syst. Res., Vol. IV, Symp. Vienna 1978,
269-277, 1978.
R.G. Strongin.
Randomization of strategies in the search for a global extremum.
In Problems of random search, 2 (Russian), pages 19-29, 219.
Izdat. "Zinatne", Riga, 1973.
R.G. Strongin.
Numerical methods in multi-extremal problems
(informational-statistical algorithms). (Chislennye metody v
mnogoehkstremal'nykh zadachakh (informatsionno-statisticheskie algoritmy)).
Moskva: "Nauka"., 1978.
Cyrille Strugarek.
On the fortet-mourier metric for the stability of stochastic
optimization problems, an example.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
V.P. Suckov and A.M. Cirlin.
Aufgaben der nichtlinearen Programmierung im Mittel und Verteilung
von Belastungen zwischen parallelen Aggregaten.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1972, No.3, 17-24, 1972.
Yasuo Sugai and Hironori Hirata.
Hierarchical algorithm for a partition problem using simulated
annealing: application to placement in VLSI layout.
Internat. J. Systems Sci., 22(12):2471-2487, 1991.
A.G. Suharev.
Ueber optimale Strategien des Suchens eines Extremums.
Zh. vycislit. Mat. Mat. Fiz. 11, 910-924, 1971.
De Feng Sun and Jin De Wang.
An approximate method for stochastic programming with recourse.
Math. Numer. Sinica, 16(1):80-92, 1994.
Jie Sun and Xinwei Liu.
Scenario formulation of stochastic linear programs and the
homogeneous self-dual interior-point method.
INFORMS J. Comput., 18(4):444-454, 2006.
Jie Sun, Kwan Eng Wee, and Jishan Zhu.
An interior point method for solving a class of linear-quadratic
stochastic programming problems.
In Recent advances in nonsmooth optimization, pages 392-404.
World Sci. Publishing, River Edge, NJ, 1995.
Jie Sun and Jishan Zhu.
A predictor-corrector method for extended linear-quadratic
programming.
Comput. Oper. Res., 23(8):755-767, 1996.
Shui Ling Sun.
A consistency check method for judgment matrices in stratified
analyses.
Qufu Shifan Daxue Xuebao Ziran Kexue Ban, 17(3):24-26, 1991.
Zai Dong Sun.
A dual algorithm for probabilistic constrained nonlinear programming
problems and its convergence.
Qufu Shifan Daxue Xuebao Ziran Kexue Ban, 21(5):107-111, 1995.
Sumit Sur and Pradip K. Srimani.
Topological properties of star graphs.
Comput. Math. Appl. 25, No.12, 87-98, 1993.
D.V. Surnachev.
Stochastic analog of Newton's method.
Mosc. Univ. Comput. Math. Cybern. 1982, No.3, 49-53, 1982.
D.V. Surnachev.
Stochastic analogue of Newton's method.
Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet.,
3:40-43, 1982.
Ju. A. Suskov.
A certain way of organizing a random search.
In Operations research and statistical modeling, No. 1
(Russian), pages 180-186. Izdat. Leningrad. Univ., Leningrad, 1972.
V.B. Svecinskii.
Random search in probabilistic iterative algorithms.
Avtomat. i Telemeh., 1:85-89, 1971.
J.M. Swaminathan and S. R. Tayur.
Managing broader product lines through delayed differentiation using
vanilla boxes.
Working Paper, GSIA, Carnegie Mellon University, Pittsburgh,
PA, July 1995, revised March 1996.
Chaitanya Swamy and David Shmoys.
Approximation Algorithms for 2-stage and Multi-stage Stochastic
Optimization.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
K. Swarup.
On duality in non-linear fractional programming problems.
Z. Angew. Math. Mech., 54:734, 1974.
K. Swarup, S.P. Aggarwal, and R.K. Gupta.
Stochastic indefinite quadratic programming.
Z. Angew. Math. Mech., 52:371-373, 1972.
Arthur J. Swersey and Edward J. Ignall, editors.
Delivery of urban services. With a view towards applications in
management science and operations research.
TIMS Studies in the Management Sciences, Vol. 22. Amsterdam etc.:
North-Holland., 1986.
E.I. Sychev and V.N. Chramenkov.
Investigation of a random search algorithm with adaptive structure
as for optimization of functions that do not fit the method of statistical
experiment.
In Applications of random search to the solution of applied
problems, Interuniv. Collect. sci. Works, Kemerovo 1981, 23-28, 1981.
W. Syski.
A method of stochastic subgradients with complete feedback stepsize
rule for convex stochastic approximation problems.
J. Optim. Theory Appl., 59(3):487-504, 1988.
Wojciech Syski.
Stochastic approximation method with subgradient filtering and
on-line stepsize rules for nonsmooth, nonconvex and unconstrained problems.
Control Cybernet., 16(1):59-77, 1987.
L.P. Sysoev.
Training procedures which combine stochastic approximation and
minimization of the empirical risk.
Automat. Remote Control, 34(3, part 1):398-411, 1973.
K. Szajowski.
Optimal stopping of a sequence of maxima over an unobservable
sequence of maxima.
Zastosow. Mat. 18, 359-374, 1984.
T. Szantai.
Evaluation of a special multivariate gamma distribution function.
Math. Program. Study 27, 1-16, 1986.
T. Szántai.
A computer code for solution of probabilistic-constrained stochastic
programming problems.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 229-235. Springer, Berlin, 1988.
T. Szántai.
Bounds for the reliability of
k-out-of-connected-(r,s)-from-(m,n)\colon F lattice systems.
In Stochastic programming methods and technical applications
(Neubiberg/Munich, 1996), pages 223-237. Springer, Berlin, 1998.
Tamás Szántai.
On the numerical solution of the stochastic programming model
STABIL of Prékopa.
Alkamaz. Mat. Lapok, 2(1-2):93-101, 1976.
D. Szasz.
Ueber ein nichtlineares Optimierungsproblem.
Studia Sci. math. Hungar. 93-100 (1975)., 1974.
Krzysztof Szkatula.
The growth of multi-constraint random knapsack with various
right-hand sides of the constraints.
Eur. J. Oper. Res. 73, No.1, 199-204, 1994.
Wlodzimierz Szkutnik.
(e, a)-confidence set of solutions of a linear
stochastic programming problem.
Przeglk ad Statyst., 25(3):441-449 (1979), 1978.
Wlodzimierz Szkutnik.
On the solution of stochastic linear programming problems as
E-models with statistical constraints.
Przegl. Stat. 32, 137-150, 1985.
Adam Szuba.
Stochastic method of searching for a global minimum.
Arch. Automat. Telemech., 25(2):179-188, 1980.
S. Takriti and J. R. Birge.
Using integer programming to refine Lagrangian-based unit
commitment solutions.
IEEE Transactions on Power Systems, 15(1):151-156, 2000.
S. Takriti and J.R. Birge.
Lagrangian solution techniques and bounds for loosely-coupled
mixed-integer stochastic programs.
Technical report, University of Michigan, 1995.
S. Takriti, J.R. Birge, and E. Long.
A stochastic model of the unit commitment problem.
IEEE Transactions on Power Systems 11(3):1497-1508, 1996.
S. Takriti, B. Krasenbrink, and L. S. Y. Wu.
Incorporating fuel constraints and electricity spot prices into the
stochastic unit commitment problem.
Operations Research, 48(2):268-280, 2000.
S. Takriti, C. Supatgiat, and L. S. Y. Wu.
Coordinating fuel inventory and electric power generation under
uncertainty.
IEEE Transactions on Power Systems, 16(4):603-608, 2001.
Samer Takriti and Shabbir Ahmed.
On robust optimization of two-stage systems.
Math. Program., 99(1, Ser. A):109-126, 2004.
Samer Takriti and John R. Birge.
Lagrangian solution techniques and bounds for loosely coupled
mixed-integer stochastic programs.
Oper. Res., 48(1):91-98, 2000.
Samer Takriti and John R. Birge.
Lagrangian solution techniques and bounds for loosely coupled
mixed-integer stochastic programs.
Oper. Res., 48(1):91-98, 2000.
Mitsushi Tamaki.
A generalized secretary problem with uncertain employment.
S¯urikaisekikenky¯usho K¯oky¯uroku, 864:154-163, 1994.
Mathematical structure of optimization theory (Japanese) (Kyoto,
1993).
E. Tamm.
Extremum problems depending on a random parameter.
In Stochastic optimization, Proc. Int. Conf., Kiev/USSR 1984,
Lect. Notes Control Inf. Sci. 81, 585-590, 1986.
Ebu Tamm.
The quasiconvexity of the probability function and the quantile
function.
Eesti. NSV Tead. Akad. Toimetised Füüs.-Mat.,
25(2):141-145, 1976.
Ebu Tamm.
Cebysev type inequalities for the solution of E-models of
nonlinear stochastic programming.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat.,
27(4):448-450, 1978.
Ebu Tamm.
On the minimization of the probability function.
Izv. Akad. Nauk Ehst. SSR, Fiz., Mat. 28, 17-24, 1979.
Ebu Tamm.
Inequalities for the solutions of nonlinear programming problems
depending on a random parameter.
Math. Operationsforsch. Statist. Ser. Optim., 11(3):487-497,
1980.
Ebu Tamm.
On minimization of a function under an equality chance constraint.
Math. Operationsforsch. Statist. Ser. Optim., 12(2):253-262,
1981.
Ebu Tamm.
Solvability of a problem of nonlinear programming with a random
parameter.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat.,
30(3):220-225, 294, 1981.
Ebu Tamm.
Approximation of a random solution in extremum problems.
Kybernetika (Prague), 23(6):483-488, 1987.
Ebu Tamm.
Approximation of an unconstrained minimum using biased estimate of
the goal function.
Eesti Tead. Akad. Toimetised Füüs. Mat., 41(1):1-6, 74,
1992.
Ebu Tamm.
On statistical estimation of a probability function.
Eesti Tead. Akad. Toimetised Füüs. Mat., 41(4):248-252,
1992.
Ehbu Tamm.
On the solvability of a problem of nonlinear programming with a
random parameter.
Izv. Akad. Nauk Ehst. SSR, Fiz., Mat. 30, 220-225, 1981.
K. Tammer.
Loesungsbegriffe und Loesungsmoeglichkeiten fuer
Optimierungsprobleme mit zufallsbehafteter Zielfunktion.
In 21. int. wiss. Kolloq.; Ilmenau 1976, Heft 3, 41-44, 1976.
K. Tammer.
On the solution of the distribution problem of stochastic
programming.
In Progress in operations research, Vols. I, II (Proc. Sixth
Hungarian Conf., Eger, 1974), pages 907-920. Colloq. Math. Soc. János
Bolyai, Vol. 12, Amsterdam, 1976. North-Holland.
K. Tammer.
Behandlung stochastischer Optimierungsprobleme unter dem
Gesichtspunkt der Strategie der Vektoroptimierung.
Wiss. Z., Tech. Hochsch. Leipz. 4, 295-302, 1980.
Klaus Tammer.
Allgemeine Lösungsbegriffe für stochastische
Optimierungsprobleme mit festem Restriktionsbereich.
Wiss. Z. Humboldt-Univ. Berlin Math.-Natur. Reihe,
26(5):595-602, 1977.
Klaus Tammer.
Relations between stochastic and parametric programming for decision
problems with a random objective function.
Math. Operationsforsch. Statist. Ser. Optim., 9(4):523-535,
1978.
Klaus Tammer.
Ueber den Zusammenhang von parametrischer Optimierung und
Entscheidungsproblemen der stochastischen Optimierung.
In Anwendungen der linearen parametrischen Optimierung,
76-91, 1979.
Kunio Tanabe.
The conjugate gradient method for computing all the extremal
stationary probability vectors of a stochastic matrix.
Ann. Inst. Statist. Math., 37(1):173-187, 1985.
H. Tanaka, H. Ichihashi, and K. Asai.
Decision and information in fuzzy linear programming problems.
In Analysis of fuzzy information, Vol. 3: Appl. eng. sci.,
265-271, 1987.
Yutaka Tanaka.
Optimal scaling for arbitrarily ordered categories.
Ann. Inst. Statist. Math., 31(1):115-124, 1979.
Hao Tang and Elise Miller-Hooks.
Solving a generalized traveling salesperson problem with stochastic
customers.
Comput. Oper. Res., 34(7):1963-1987, 2007.
Hao Tang, Hong Sheng Xi, and Bao Qun Yin.
On-line optimization algorithm for Markov control processes based
on a single sample path.
Control Theory Appl., 19(6):865-871, 2002.
Heng Yong Tang.
A dual parallel algorithm for stochastic linear programs with simple
recourse.
Acta Math. Appl. Sinica, 12(3):362-367, 1989.
Heng Yong Tang.
A dual contact plane algorithm for problems with probabilistic
constraints.
Acta Math. Appl. Sinica, 18(1):147-151, 1995.
Hengyong Tang.
Penalty function methods for two-period stochastic linear programs
with simple recourse.
Numer. Math., Nanking 9, No.3, 285-288, 1987.
Li Xin Tang, Zi Hou Yang, and Meng Guang Wang.
The lot sizing problem for MRP-II under CIMS.
Control Theory Appl., 14(3):376-382, 1997.
Qian-Yu Tang, Pierre L'Ecuyer, and Han-Fu Chen.
Central limit theorems for stochastic optimization algorithms using
infinitesimal perturbation analysis.
Discrete Event Dyn. Syst., 10(1-2):5-32, 2000.
Z. B. Tang.
Optimal sequential sampling policy of partitioned random search and
its approximation.
J. Optim. Theory Appl., 98(2):431-448, 1998.
Z. Bo Tang.
Adaptive partitioned random search to global optimization.
IEEE Trans. Automat. Control, 39(11):2235-2244, 1994.
Matthew W. Tanner and Eric B. Beier.
Tabu search for probabilistically constrained stochastic programs
with application to vaccine allocation.
Optimization Online, http://www.optimization-online.org, 2007.
Charles S. Tapiero.
Optimal control of a stochastic model of advertising.
In Optimal control theory and economic analysis, Workshop,
Vienna 1981, 287- 300, 1982.
Charles S. Tapiero, Arnold Reisman, and Peter Ritchken.
Product failures, manufacturing reliability and quality control: A
dynamic framework.
INFOR 25, 152-164, 1987.
Charles S. Tapiero and Agnes Sulem.
Computational aspects in applied stochastic control.
Comput. Econ. 7, No.2, 109-146, 1994.
George S. Tarasenko.
Stochastic optimization in the Soviet Union.
Monograph Series on Soviet Union. Delphic Associates, Falls Church,
VA, 1985.
Random search algorithms, With a foreword by Lennart Ljung.
G.S. Tarasenko.
Estimate of the convergence rate of an adaptive random search method.
Problemy Sluchain. Poiska, 8:162-185, 334, 1980.
Voprosy Strukturnoi Adaptatsii.
R. Tarres.
Asymptotic evolution of a stochastic control problem when the
discount vanishes.
Asterisque 75-76, 227-237, 1980.
R. Tavast.
Estimation of the admissible set in programming problems with
stochastic constraints.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat., 20:478-480,
1971.
S. Tayur.
A new algorithm to solve stochastic integer programs with application
to plant management.
Technical report, Carnegie Mellon University, Pittsburgh, (in
preparation).
S.R. Tayur, R.R. Thomas, and N.R. Natraj.
An algebraic geometry algorithm for scheduling in the presence of
setups and correlated demands.
Mathematical Programming, 69(3):369-401, 1995.
J. Teghem.
"STRANGE": an interactive method for multiobjective
stochastic linear programming, and "STRANGE-MOMIX":
its extension to integer variables.
In Stochastic versus fuzzy approaches to multiobjective
mathematical programming under uncertainty, volume 6 of Theory Decis.
Lib. Ser. D System Theory Knowledge Engrg. Probl. Solving, pages 103-115.
Kluwer Acad. Publ., Dordrecht, 1990.
J. Teghem.
New developments in multiobjective stochastic linear programming.
In Applied stochastic models and data analysis, Vol. I, II
(Chania, 1993), pages 938-948. World Sci. Publishing, River Edge, NJ, 1993.
J.jun. Teghem, D. Dufrane, M. Thauvoye, and P. Kunsch.
STRANGE: An interactive method for multi-objective linear
programming under uncertainty.
Eur. J. Oper. Res. 26, 65-82, 1986.
Jr. Teghem, Jacques.
Multiobjective and stochastic linear programming.
Found. Control Engrg., 8(3-4):225-232 (1984), 1983.
Special issue on the 18th meeting of the EURO working group on
multicriteria decision aid (Pozna\'n, 1983).
Tejada-Guibert, S. A. Johnson, and J. R. Stedinger.
The value of hydrologic information in stochastic dynamic programming
models of a multireservoir system.
Water Resources Research, 31:2571-2579, 1995.
D. Teneketzis, H. Javid, and B. Sridhar.
Operations scheduling by Markov chains with strong and weak
interactions.
In Electric power problems: the mathematical challange, Proc.
Conf., Seattle/Wash. 1980, 426-436, 1980.
L. A. Terry, M. V. F. Pereira, T. A. Araripe Neto, L. E. C. A. Pinto, and
P. R. H. Sales.
Coordinating the energy generation of the Brazilian national
hydrothermal electrical generating system.
INTERFACES, 16(1):16-38, 1986.
Vitautas Teshis.
On the numerical rate and efficiency of some global minimization
algorithms.
Teor. Optim. Reshenij 6, 95-105, 1980.
M.A.L. Thathachar and K.R. Ramakrishnan.
On line optimization with a team of learning automata.
In Theory and application of digital control, Proc. IFAC Symp.,
New Delhi 1982, 297-302, 1982.
J. Thénié, Ch. van Delft, and J.-Ph. Vial.
Automatic formulation of stochastic programs via an algebraic
modeling language.
Comput. Manag. Sci., 4(1):17-40, 2007.
Julien Thénié and Jean-Philippe Vial.
Step decision rules for multistage stochastic programming: a
heuristic approach.
Optimization Online, http://www.optimization-online.org, 2006.
R. Theodorescu.
Minimax solutions of random convex programs.
Atti Accad. Naz. Lincei, Rend., Cl. Sci. Fis. Mat. Nat., VIII.
Ser. 46, 689-692, 1969.
R. Theodorescu.
Random programs.
In Math. Aspects Life Sci., Queen's Papers pure appl. Math. 26,
20-94, 1971.
Radu Theodorescu.
Random programs.
Math. Operationsforsch. Statist., 3(1):19-47, 1972.
Radu Theodorescu.
The minimax principle and random programs.
Rend. Circ. Mat. Palermo (2), 28(1):151-160, 1979.
Lionel Thibault.
Espérances conditionnnelles d'intégrandes lipschitziens et
d'intégrandes semi-continus inférieurement.
C.R. Acad. Sci. Paris Sér. A-B, 291(9):A551-A554, 1980.
P. Thoft-Christensen.
Application of stochastic methods in optimization.
Wiss. Z. Hochschule Architektur Bauwesen Weimar 22, 239-244,
1975.
P. Thoft-Christensen.
Stochastic modeling and optimization of complex infrastructure
systems.
In System modeling and optimization, volume 166 of IFIP
Int. Fed. Inf. Process., pages 109-122. Kluwer Acad. Publ., Boston, MA,
2005.
G.L. Thompson, W.W. Cooper, and A. Charnes.
Characterizations by chance-constrained programming.
In Recent Advances math. Program., 113-120, 1963.
Howard E. Thompson, John P. Matthews, and Bob C.L. Li.
Insurance exposure and investment risks: An analysis using chance-
constrained programming.
Operations Res. 22, 991-1007, 1974.
John T. Thorpe and Jr. Harris, Frederick C.
A parallel stochastic optimization algorithm for finding mappings of
the rectilinear minimal crossing problem.
Ars Combin., 43:135-148, 1996.
G. Thurner.
Stochastische Optimierung des Ablaufs von Linienbaustellen.
In VI. internat. Kongr. Anwend. Math. Ingenieurwiss., Weimar
1972, 138-142 , 1974.
St. Tigan and I.M. Stancu-Minasian.
The stochastic max-min problem.
In Contributions to operations research and mathematical
economics, Vol. I, volume 51 of Methods Oper. Res., pages 119-126.
Athenäum/Hain/Hanstein, Königstein/Ts., 1984.
Stefan Tigan and I.M. Stancu-Minasian.
The stochastic max-min problem.
Cahiers Centre Études Rech. Opér., 27(3-4):247-254, 1985.
Stefan Tigan and I.M. Stancu-Minasian.
The stochastic bottleneck transportation problem.
Math., Rev. Anal. Numer. Theor. Approximation, Anal. Numer.
Theor. Approximation 14, 153-158, 1985.
Stephan Tigan and I.M. Stancu-Minasian.
Methods for solving stochastic bilinear fractional max-min problems.
RAIRO Rech. Opér., 30(1):81-98, 1996.
A.S. Tikhomirov.
Markov sequences as optimization algorithms.
In Model-oriented data analysis (Petrodvorets, 1992), Contrib.
Statist., pages 249-256. Physica, Heidelberg, 1993.
A.S. Tikhomirov.
Combinations of global and local search methods as optimization
algorithms.
Zh. Vychisl. Mat. i Mat. Fiz., 36(9):50-59, 1996.
A.N. Tikhonov, F.P. Vasil'ev, M.M. Potapov, and A.D. Yurij.
Regularization of minimization problems on approximately specified
sets.
Mosc. Univ. Comput. Math. Cybern. 1977, No.1, 2-14 translation
from Vestn. Mosk. Univ., Ser. XV 1977, No.1, 4-19 (1977)., 1977.
G. Timmel.
Modifikationen eines statistischen Suchverfahrens der
Vektoroptimierung.
Wiss. Z. Tech. Hochsch. Ilmenau, 28(5):139-148, 1982.
G. A. Timofeeva.
Efficient control in multistage stochastic optimization problem.
In Proceedings of the 4th International Conference on
Mathematical Methods in Operations Research and 6th Workshop on
Well-posedness and Stability of Optimization Problems (Sozopol, 1997),
volume 12, pages 235-244, 1998.
G. A. Timofeeva.
Correction of efficient solutions in a multistep stochastic
optimization problem.
Izv. Akad. Nauk Teor. Sist. Upr., (1):48-54, 2001.
F. Tin-Loi, L. Qi, Z. Wei, and R.S. Womersley.
Stochastic ultimate load analysis: models and solution methods.
Numer. Funct. Anal. Optim., 17(9-10):1029-1043, 1996.
C.-J. Ting and P. Schonfeld.
Optimization through simulation of waterway transportation
investments.
Transportation Research Record, no. 1620:11-16, 1998.
K.A. Tinn and È. H. Tyugu.
Nonlinear programming with random constraints.
Kibernetika (Kiev), 1:54-62, 1968.
Gerhard Tintner.
Stochastic programming and stochastic control.
Trab. Estad. Invest. Oper. 26, 487-499, 1975.
Gerhard Tintner and M.V.Rama Sastry.
A note on the use of nonparametric statistics in stochastic linear
programming.
Management Sci., Appl. 19, 205-210, 1972.
Gerhard Tintner and Jati K. Sengupta.
Stochastic economics. Stochastic processes, control, and
programming.
New York-London: Academic Press., 1972.
Cristian-Ioan Tiu and Thaleia Zariphopoulou.
On level curves of value functions in optimization models of expected
utility.
Math. Finance, 10(2):323-338, 2000.
INFORMS Applied Probability Conference (Ulm, 1999).
R.N. Tiwari, S. Dharmar, and J.R. Rao.
Fuzzy goal programming - an additive model.
Fuzzy Sets Syst. 24, 27-34, 1987.
Pavel Tlusty.
Deviations from asymptotic normality for optimal solutions of
stochastic programming problems.
Commentat. Math. Univ. Carol. 33, No.1, 188, 1992.
T. Tobias.
Kuhn-Tucker conditions for the two-phase stochastic programming
problem with integral constraints.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat., 27(1):3-8,
1978.
T. Tobias.
Lagrange multipliers in linear multistage stochastic programming
problems.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat., 28(2):97-100,
1979.
T. Tobias.
Approximation method for the solution of programming problems with
operator constraints.
Eesti NSV Tead. Akad. Toimetised Füüs.-Mat.,
29(1):101-104, 111, 1980.
M. J. Todd.
Erratum: "Probabilistic models for linear programming" [Math.\
Oper. Res. 16 (1991), no. 4, 671-693; MR 93a:90042].
Math. Oper. Res., 23(3):767-768, 1998.
Michael J. Todd.
Probabilistic models for linear programming.
Math. Oper. Res., 16(4):671-693, 1991.
Yesim Tokat, Svetlozar T. Rachev, and Eduardo Schwartz.
The stable non-Gaussian asset allocation: A comparison with the
classical Gaussian approach.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Berkin Toktas, Joyce W. Yen, and Zelda B. Zabinsky.
A stochastic programming approach to resource-constrained assignment
problems.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
B. Tolwinski.
The approximate method for solving dynamic optimisation problems.
In 18. internat. wiss. Kolloqu., Ilmenau 1, 65-67, 1973.
A. Tomasgard, J.A. Audestad, S. Dye, L. Stougie, M.H. van der Vlerk, and S.W.
Wallace.
Modelling aspects of distributed processing in telecommunication
networks.
Annals of Operations Research, 82:161-184, 1998.
A. Tomasgard, S. Dye, S.W. Wallace, J.A. Audestad, L. Stougie, and M.H. van
der Vlerk.
Stochastic optimization models for distributed communication
networks.
Working paper, Department of Industrial Economics and Technology
Management, Norwegian University of Science and Technology, 7034 Trondheim,
Norway, 1997.
Nikolas Topaloglou, Hercules Vladimirou, and Stavros A. Zenios.
CvaR models with selective hedging for international asset
allocation.
Journal of Banking and Finance, 26(7):1535-1561, 2002.
Huseyin Topaloglu and Warren B. Powell.
An algorithm for approximating piecewise linear concave functions
from sample gradients.
Oper. Res. Lett., 31(1):66-76, 2003.
V.V. Topka.
A probabilistic model for planning research and development under
linear constraints.
Avtomat. i Telemekh., 4:232-237, 1997.
A.F. Torondzhadze.
On an information-statistical method for solving multi-extremal
problems.
Tr., Vychisl. Tsentr Im. N. I. Muskhelishvili 21, No.2, 56-66,
1981.
Vassili V. Toropov.
Multipoint approximation method for structural optimization problems
with noisy function values.
In Stochastic programming (Neubiberg/München, 1993), volume
423 of Lecture Notes in Econom. and Math. Systems, pages 109-122.
Springer, Berlin, 1995.
Norio Baba Toshio Shoman and Yoshikazu Sawaragi.
A modified convergence theorem for a random optimization method.
Inform. Sciences 13, 159-166, 1977.
Tasuku Toyonaga, Takeshi Itoh, and Hiroaki Ishii.
A crop planning problem with fuzzy random profit coefficients.
Fuzzy Optim. Decis. Mak., 4(1):51-69, 2005.
Jui-Fen C. Trappey, C.Richard Liu, and Tien-Chien Chang.
Fuzzy nonlinear programming: Theory and application in
manufacturing.
Int. J. Prod. Res. 26, No.5, 975-985, 1988.
Grigori L. Tretiakov.
Star-shaped approximation approach for stochastic programming
problems with probability function.
Optimization, 47(3-4):303-317, 2000.
Numerical methods for stochastic optimization and real-time control
of robots (Neubiberg/Munich, 1998).
Grigoriy Tretiakov.
Stochastic quasi-gradient algorithms for maximization of the
probability function. A new formula for the gradient of the probability
function.
In Stochastic optimization techniques (Neubiberg/Munich, 2000),
volume 513 of Lecture Notes in Econom. and Math. Systems, pages
117-139. Springer, Berlin, 2002.
V.E. Tret'yakov.
Program synthesis in a stochastic differential game.
Sov. Math., Dokl. 27, 610-614 translation from Dokl. Akad. Nauk
SSSR 270, 297-300 (1983)., 1983.
V.E. Tret'yakov.
Synthesis of optimal guaranteeing control.
Probl. Control Inf. Theory 17, No.4, 207-221, 1988.
M.D. Troutt and T.B. Paine.
A monotone variational method for optimal probability distributions.
OR Spektrum, 12(4):201-206, 1990.
Alain Trouvé.
Noyaux partiellement synchrones et parallélisation du recuit
simulé.
C.R. Acad. Sci. Paris Sér. I Math., 312(1):155-158, 1991.
A. Truffert.
Conditional expectation of integrands and random sets.
Ann. Oper. Res., 30(1-4):117-156, 1991.
Stochastic programming, Part I (Ann Arbor, MI, 1989).
G. Trzpiot.
Multivalued limit laws applied to stochastic optimization.
Random Oper. Stochastic Equations, 3(4):309-314, 1995.
P. Tsamasphyrou, A. Renaud, and P. Carpentier.
Transmission network planning under uncertainty with benders
decomposition.
Lecture Notes in Economics and Mathematical Systems,
481:457-472, 2000.
H.S.J. Tsao, S.C. Fang, and D.N. Lee.
On the optimal entropy analysis.
Eur. J. Oper. Res. 59, No.2, 324-329, 1992.
Chung-Li Tseng and Graydon Barz.
Short-term generation asset valuation: a real options approach.
Oper. Res., 50(2):297-310, 2002.
A.M. Tsirlin.
Conditions for optimality of the solution of averaged problems of
mathematical programming.
Dokl. Akad. Nauk, 323(1):43-47, 1992.
G. Sh. Tsitsiashvili.
Parallelization of stochastic programming algorithms.
Issled. Operatsii i ASU, 35:3-11, 1991.
G.Sh. Tsitsiashvili.
Decomposition methods in problems of stability and efficiency
of complex systems. (Dekompozitsionnye metody v zadachakh ustojchivosti i
ehffektivnosti slozhnykh sistem).
Vychislitel'nyj Tsentr Dal'nevostochnogo Otdeleniya AN SSSR,
Vladivostok, 1989.
John N. Tsitsiklis.
A survey of large time asymptotics of simulated annealing algorithms.
In Stochastic differential systems, stochastic control theory
and applications (Minneapolis, Minn., 1986), volume 10 of IMA Vol.
Math. Appl., pages 583-599. Springer, New York, 1988.
È. V. Tsoi.
Solvability of certain classes of multistage stochastic programming
problems with arbitrary information structure.
Cybernetics, 16(4):619-620 (1981), 1980.
Eh.V. Tsoj.
On a class of infinite-stage problems of stochastic programming.
Kibernetika 1980, No.5, 144-145, 1980.
Ioannis G. Tsoulos and Isaac E. Lagaris.
Genetically controlled random search: a global optimization method
for continuous multidimensional functions.
Comput. Phys. Comm., 174(2):152-159, 2006.
V.I. Tsurkov.
A two-stage stochastic programming problem of the block type.
U.S.S.R. Comput. Math. Math. Phys. 18, No.2, 66-75, 1978.
Ya. Z. Tsypkin.
Adaptive decision-making methods under uncertainty conditions.
Automat. Remote Control, 37(4, part 1):544-556, 1976.
Ya. Z. Tsypkin, A.I. Kaplinskiy, and A.S. Krasnenker.
Methods of local improvement in stochastic optimization problems.
Engrg. Cybernetics, 11(6):889-897, 1973.
Ya. Z. Tsypkin and A.S. Poznyak.
Optimal and robust optimization algorithms in the presence of
correlated noise.
Dokl. Akad. Nauk SSSR, 258(6):1330-1333, 1981.
Ya. Z. Tsypkin and A.S. Poznyak.
Optimal search algorithms for stochastic optimization.
Dokl. Akad. Nauk SSSR, 260(3):550-553, 1981.
Ya. Z. Tsypkin and A.S. Poznyak.
Recursive algorithms for optimization under conditions of
uncertainty.
In Engineering cybernetics, Vol. 16, Itogi Nauki i Tekhniki,
pages 3-70. Akad. Nauk SSSR Vsesoyuz. Inst. Nauchn. i Tekhn. Inform.,
Moscow, 1983.
Ya.Z. Tsypkin.
Optimality in problems and optimization algorithms under
indeterminacy.
Autom. Remote Control 47, 67-72 translation from Avtom.
Telemekh. 1986, No.1, 75-80 (1986)., 1986.
Ya.Z. Tsypkin, A.S. Poznyak, and A.M. Pesin.
Search algorithms for criterial optimization under conditions of
uncertainty.
Sov. Phys., Dokl. 28, 367-369 translation from Dokl. Akad. Nauk
SSSR 270, 565-568 (1983)., 1983.
A.D. Tuniev.
A certain approach to the solution of stochastic programming
problems.
In Theory of optimal solutions (Proc. Sem., Kiev, 1968), No. 3
(Russian), pages 42-49, Kiev, 1969. Akad. Nauk Ukrain. SSR.
A. Turgeon.
Optimal operation of multireservoir power systems with stochastic
inflows.
Water Resources Research, 16:275-283, 1980.
Alexandre Turull.
Clifford theory with Schur indices.
J. Algebra 170, No.2, 661-677, 1994.
E. Ubi.
Finding an optimal random solution of M-and P-models in stochastic
programming.
Izv. Akad. Nauk Ehst. SSR, Fiz. Mat. 26, No.1, 64-71, 1977.
E. Uebi.
Statistical investigation of stochastic programming problems and a
method for their solution.
Izv. Akad. Nauk Ehst. SSR, Fiz. Mat. 26, 369-375, 1977.
Fevzi Uenlue.
Unconstrained probabilistic goal programming by the least square
adjustment to finite convolution.
In Applications of mathematics in system theory, Proc. int.
Symp., Brasov/Romania 1978, Vol. II, 189-195, 1979.
Takayuki Ueno and Seiichi Iwamoto.
On past accumulation values and future threshold values in stochastic
optimization.
S¯urikaisekikenky¯usho K¯oky¯uroku, (1207):79-100, 2001.
Perspective and problems for dynamic programming with uncertainty
(Japanese) (Kyoto, 2001).
B. Uhrin.
Brunn-Minkowski-Lusternik inequality, its sharpenings, extensions
and some applications.
Közl. MTA Számitástech. Automat. Kutató Int. Budapest,
39:151-194, 1988.
B. Uhrin.
Two types of random polyhedral sets.
Közl. MTA Számitástech. Automat. Kutató Int. Budapest,
39:131-149, 1988.
S. Ul'm.
Zu Methoden der hierarchischen Optimierung.
Izv. Akad. Nauk Eston. SSR, Fiz. Mat. 21, 208-211, 1972.
Universidad de la Habana Departamento de Matemática Aplicada.
Workshop on Stochastic Optimization: the State of the
Art, Havana, 1993.
Papers from the workshop held in Havana, April 1992, Investigación
Oper. 14 (1993), no. 2-3.
S. Urasiev.
Adaptive stochastic quasigradient procedures.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 373-384. Springer, Berlin, 1988.
Andrzej Urbaniak.
A method of a choice of an expansion strategy of the water supply
system with random parameters.
Found. Control Eng. 10, 143-159, 1985.
S.P. Urjas'ev.
Adaptive control of parameters in gradient algorithms for stochastic
optimization.
In Stochastic optimization (Kiev, 1984), volume 81 of
Lecture Notes in Control and Inform. Sci., pages 591-601. Springer, Berlin,
1986.
Bruno Urli and Raymond Nadeau.
Multiobjective stochastic linear programming with incomplete
information: a general methodology.
In Stochastic versus fuzzy approaches to multiobjective
mathematical programming under uncertainty, volume 6 of Theory Decis.
Lib. Ser. D System Theory Knowledge Engrg. Probl. Solving, pages 131-161.
Kluwer Acad. Publ., Dordrecht, 1990.
Bruno Urli and Raymond Nadeau.
Stochastic MOLP with incomplete information: An interactive approach
with recourse.
J. Oper. Res. Soc. 41, No.12, 1143-1152, 1990.
Veniamin Al. Urseanu.
Models of stochastic optimization and the price systems.
Econom. Comput. econom. Cybernetics Studies Res. 1973, Nr. 4,
63-76, 1973.
S. Uryasev.
Analytic perturbation analysis of discrete event dynamic systems.
In Proc. Fourth International Conference on Computer Integrated
Manufacturing & Automated Technology, pages 397-402, Troy, NY., 1994. IEEE
Computer Society Press.
S. Uryasev.
Derivatives of probability functions and integrals over sets given by
inequalities.
J. Computational and Applied Mathematics, 56:197-223, 1994.
S. Uryasev.
New formulas for the derivatives of integrals and application to a
shut down problem.
In Proc. International Conference on Mathematics and
Computations, Reactor Physics, and Environmental Analyses, pages 689-698,
Portland, Oregon, 1995. American Nuclear Society.
S. Uryasev and Vallerga H.
Optimization of test strategies - a general approach.
Reliability Engineering & Systems Safety, 41:155-165, 1993.
S.P. Uryas'ev.
Step control for direct stochastic-programming methods.
Cybernetics, 16(6):886-890 (1981), 1980.
S.P. Uryas'ev.
On step length adjustment in limiting extremum problems.
Cybernetics, 17(1):117-121, 1981.
S.P. Uryas'ev.
Differentiability of the integral over a set defined by inclusion.
Cybernetics 24, No.5, 638-642 translation from Kibernetika 1988,
No.5, 83-86 (1988)., 1988.
S.P. Uryas'ev.
Adaptive algorithms of stochastic optimization and game theory.
(Adaptivnye algoritmy stokhasticheskoj optimizatsii i teorii igr). Ed. by Yu.
M. Ermol'ev.
Nauka, Moskwa, 1990.
S.P. Uryas'ev.
Adaptivnye algoritmy stokhasticheskoi optimizatsii i
teorii igr.
"Nauka", Moscow, 1990.
Edited and with a preface by Yu. M. Ermol' ev, With an English
summary.
S.P. Uryas'ev.
A stochastic quasigradient algorithm with variable metric.
Ann. Oper. Res., 39(1-4):251-267 (1993), 1992.
Stanislav Uryasev.
Derivatives of probability functions and some applications.
Ann. Oper. Res., 56:287-311, 1995.
Stochastic programming (Udine, 1992).
Stanislav Uryasev and Panos M. Pardalos, editors.
Stochastic optimization: algorithms and applications.
Kluwer Academic Publishers, Dordrecht, 2001.
Papers from the conference held at the University of Florida,
Gainesville, FL, February 20-22, 2000.
Stanislav Uryasev and R. Tyrrell Rockafellar.
Conditional value-at-risk: optimization approach.
In Stochastic optimization: algorithms and applications
(Gainesville, FL, 2000), volume 54 of Appl. Optim., pages 411-435.
Kluwer Acad. Publ., Dordrecht, 2001.
Stanislav P. Uryasev, editor.
Probabilistic constrained optimization.
Kluwer Academic Publishers, Dordrecht, 2000.
Methodology and applications.
I.A. Ushakov and E.I. Gordienko.
Ueber ein statistisches Verfahren zur Loesung gewisser
Optimierungsprobleme.
Elektron. Informationsverarbeitung Kybernetik 14, 585-592,
1978.
N. Uteuliev.
Optimality conditions and duality relations for a class of multistage
problems of stochastic nonlinear programming.
Issled. Operatsii i ASU, 32:39-42, 117, 1988.
I. Vaduva and G. Ciobanu.
The "duration - resource" relation in PERT type problems with
resources.
Econom. Comput. econom. Cybernetics Studies Res. 1973, Nr. 1,
61-78, 1973.
A.G. Vainstein, V.P. Suckov, and A.M. Cirlin.
Ueber Abschaetzungen der Loesung des Problems der nichtlinearen
Programmierung im Mittel.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1976, Nr. 1, 29-33, 1976.
S. Vajda.
Stochastic programming.
In J. Abadie, editor, Integer and nonlinear programming, pages
321-336. North-Holland, Amsterdam, 1970.
S. Vajda.
Probabilistic programming.
Academic Press, New York, 1972.
Probability and Mathematical Statistics.
S. Vajda.
Linear programming. Algorithms and applications.
Science Paperbacks, 167. London, New York: Chapman and Hall., 1981.
Stefan Vajda.
Deterministic equivalents of probabilistic constraints.
In Proceedings of the Second International Seminar on
Operational Research in the Basque Provinces (San Sebastian, 1987), pages
253-260, Bilbao, 1988. Univ. País Vasco-Euskal Herriko Unib.
A. T. Vakhitov, O. N. Granichin, and S. S. Sysoev.
The accuracy of the estimation of a randomized stochastic
optimization algorithm.
Avtomat. i Telemekh., (4):86-96, 2006.
P. Valente, G. Mitra, and C. A. Poojari.
A stochastic programming integrated environment.
In Applications of stochastic programming, volume 5 of
MPS/SIAM Ser. Optim., pages 115-136. SIAM, Philadelphia, PA, 2005.
P. Valente, Gautam Mitra, M. Sadki, and R. Fourer.
Extending algebraic modelling languages for stochastic programming.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Bernard Van Cutsem.
Problems of convergence in stochastic linear programming.
In Techniques of optimization (Fourth IFIP Colloq., Los Angeles,
Calif., 1971), pages 445-454, New York, 1972. Academic Press.
Ch. van Delft and J.-Ph. Vial.
A practical implementation of stochastic programming: an application
to the evaluation of option contracts in supply chains.
Automatica J. IFAC, 40(5):743-756 (2005), 2004.
Maarten H. van der Vlerk.
Stochastic programming with integer recourse.
PhD thesis, University of Groningen, The Netherlands, 1995.
Maarten H. van der Vlerk.
Convex approximations for stochastic programs with simple integer
recourse.
In Ten years LNMB, volume 122 of CWI Tract, pages
357-365. Math. Centrum Centrum Wisk. Inform., Amsterdam, 1997.
Maarten H. van der Vlerk.
Simplification of recourse models by modification of recourse data.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Maarten H. van der Vlerk.
Convex approximations for complete integer recourse models.
Math. Program., 99(2, Ser. A):297-310, 2004.
Maarten H. van der Vlerk.
Simplification of recourse models by modification of recourse data.
In Dynamic stochastic optimization (Laxenburg, 2002), volume
532 of Lecture Notes in Econom. and Math. Systems, pages 321-336.
Springer, Berlin, 2004.
Maarten H. van der Vlerk.
Convex approximations for a class of mixed-integer recourse models.
Stochastic Programming E-Print Series, http://www.speps.org,
2005.
Maarten H. van der Vlerk.
Modification of Recourse Data for Mixed-Integer Recourse Models.
In S. Albers, R.H. Möhring, G.Ch. Pflug, and R. Schultz, editors,
Dagstuhl Seminar 05031: Algorithms for Optimization with Incomplete
Information, http://www.dagstuhl.de/05031, 2005.
Maarten H. van der Vlerk, Willem K. Klein Haneveld, and Matthijs H. Streutker.
Integrated chance constraints in an alm model for pension funds.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
M.H. van der Vlerk.
Stochastic programming with simple integer recourse.
In C.A. Floudas and P.M. Pardalos, editors, Encyclopedia of
Optimization, volume V, pages 343-346. Kluwer Academic Publishers, 2001.
M.H. van der Vlerk.
On multiple simple recourse models.
Mathematical Methods of Operations Research, 62(2):225-242,
2005.
J. van der Wal.
Stochastic dynamic programming. Successive approximations and
nearly optimal strategies for Markov decision processes and Markov games.
Mathematical Centre Tracts, 139. Amsterdam: Mathematisch Centrum.,
1981.
Paul van Moeseke.
Stochastic portfolio programming: the game solution.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 497-505, London, 1980. Academic Press.
Paul van Moeseke.
Efficient portfolios: risk shares and monetary policy.
In Econometrics of planning and efficiency, volume 11 of
Adv. Stud. Theoret. Appl. Econometrics, pages 109-122. Kluwer Acad. Publ.,
Dordrecht, 1988.
R. Van Slyke and R.J-B. Wets.
L-shaped linear programs with applications to control and stochastic
programming.
SIAM Journal on Applied Mathematics, 17:638-663, 1969.
A. Vande Wouwer, C. Renotte, and M. Remy.
On the use of simultaneous perturbation stochastic approximation for
neural network training.
In Proceedings of the American Control Conference, pages
388-392, 1999.
Pravin Varaiya and Roger J.-B. Wets.
Stochastic dynamic optimization approaches and computation.
In Mathematical programming (Tokyo, 1988), volume 6 of
Math. Appl. (Japanese Ser.), pages 309-331. KTK Scientific, Tokyo, 1989.
Anna Vasarhelyi.
Collapse load analysis and optimal design by stochastic programming
with uncertainties of loads.
In Stochastic optimization. Numerical methods and technical
applications, Proc. GAMM/IFIP-Workshop, Neubiberg/Ger. 1990, Lect. Notes
Econ. Math. Syst. 379, 173-182, 1992.
S.A. Vasil'kovskij.
The solution of stochastic standardization problems.
Comput. Math. Math. Phys. 32, No.2, 275-278 translation from Zh.
Vychisl. Mat. Mat. Fiz. 32, No.2, 331-335 (1992)., 1992.
A.A. Vasin and N.N. Evtikhiev.
Some decision-making problems associated with random walks.
Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet.,
1:19-23, 48, 1997.
Christiana Vassiadou-Zeniou and Stavros A. Zenios.
Robust optimization models for managing callable bond portfolios.
European Journal of Operations Research, 91:264-273, 1996.
I.A. Vatel' and V.I. Tsurkov.
A two-level system with stochastic parameter constraints.
Automat. Remote Control, 41(2, part 2):232-237, 1980.
A.A. Vatolin.
Correction of inconsistent linear inequality system with regard to
restrictions on parameter variation.
In 19. Jahrestagung "Mathematische Optimierung" (Sellin,
1987), volume 90 of Seminarberichte, pages 132-135. Humboldt Univ.
Berlin, 1987.
Felisa J. Vázquez-Abad.
Sensitivity analysis for stochastic DEDS: an overview.
In Second Symposium on Probability Theory and Stochastic
Processes. First Mexican-Chilean Meeting on Stochastic Analysis (Spanish)
(Guanajuato, 1992), volume 7 of Aportaciones Mat. Notas
Investigación, pages 163-182. Soc. Mat. Mexicana, México City, 1992.
A. S. Velichko.
A dual truncation algorithm for a two-stage stochastic programming
problem.
Izv. Vyssh. Uchebn. Zaved. Mat., (4):78-81, 2006.
G.I. Venkov.
The identification of stochastic systems as a minimax problem.
God. Vissh. Uchebn. Zaved., Prilozhna Mat. 1 No.2, 121-128
(1978)., 1977.
P.I. Vercenko.
Limit extremal problems with complex tests.
In Systems analysis by operations research and reliability
theory methods (Russian), pages 67-78, Kiev, 1975. Inst. Kibernet. Akad.
Nauk Ukrain. SSR.
P.I. Verchenko.
Solution of limit problems of stochastic programming with general
constraints.
In Methods of investigation of extremal problems, pages
96-103, 123, Kiev, 1981. Akad. Nauk Ukrain. SSR Inst. Kibernet.
J.L. Verdegay.
Fuzzy mathematical programming.
In Fuzzy information and decision processes, 231-237, 1982.
Bram Verweij, Shabbir Ahmed, Anton Kleywegt, George Nemhauser, and Alexander
Shapiro.
The sample average approximation method applied to stochastic routing
problems: A computational study.
Optimization Online, http://www.optimization-online.org, 2001.
R.P.van der Vet.
On the Bellman principle for decision problems with random decision
policies.
J. Engin. Math. 10, 107-114, 1976.
René Victor Valqui Vidal.
Stochastic version of the economic lot size model.
Investigación Oper., 15(1):3-12, 1994.
Stefan Vigerske and Ivo Nowak.
Adaptive discretization of convex multistage stochastic programs.
Math. Methods Oper. Res., 65(2):361-383, 2007.
J.P. Vila.
Exact experimental designs via stochastic optimization for nonlinear
regression models.
In Computational statistics, Proc. 9th Symp. COMPSTAT,
Dubrovnik/Yugosl. 1990, 291-296, 1990.
M.L. Vil'k and S.V. Shil'man.
Convergence and optimality of quasi-Newtonian algorithms of
stochastic optimization.
Dinamika Sistem, 151:3-21, 1985.
M.L. Vil'k and S.V. Shil'man.
Convergence and optimality of realizable algorithms for adaptation
(informational approach).
Problemy Peredachi Informatsii, 21(3):80-88, 1985.
Eh.J. Vilkas and E.Z. Majminas.
Decisions: theory, information, simulation. (Resheniya:
teoriya, informatsiya, modelirovanie).
Moskva: "Radio i Svyaz"., 1981.
I.M. Vishenchuk, V.V. Trotsenko, and B.I. Shvetskij.
Analysis of transiens processes in computing converters for integral
signal parameters.
Autom. Control Comput. Sci. 12, No.3, 36-43, 1978.
B. V. Vishnyakov and A. I. Kibzun.
Deterministic equivalents for stochastic programming problems with
probabilistic criteria.
Avtomat. i Telemekh., (6):126-143, 2006.
Béla Vizvári.
The integer programming background of a stochastic integer
programming algorithm of Dentcheva-Prékopa-Ruszczy\'nski.
Optim. Methods Softw., 17(3):543-559, 2002.
Stochastic programming.
H. Vladimirou, S.A. Zenios, and R.J.-B. Wets, editors.
Models for planning under uncertainty.
Baltzer Science Publishers BV, Amsterdam, 1995.
Ann. Oper. Res. 59 (1995).
Hercules Vladimirou.
Computational assessment of distributed decomposition methods for
stochastic linear programs.
European Journal of Operational Research, 108:653-670, 1998.
Hercules Vladimirou, Istvan Maros, and Gautam Mitra, editors.
Applied Mathematical Programming and Modeling IV.
Annals of Operations Research, Vol. 99. Kluwer Academic Publishers,
2000.
Hercules Vladimirou and Stavros A. Zenios.
Parallel algorithms for large-scale stochastic programming.
In P. M. Pardalos A. Migdalas and S. Storoy, editors, Parallel
Computing in Optimization, pages 413-461. Kluwer, Dordrecht, 1997.
Hercules Vladimirou and Stavros A. Zenios.
Stochastic linear programs with restricted recourse.
European Journal of Operations Research, 101:177-192, 1997.
Hercules Vladimirou and Stavros A. Zenios.
Stochastic programming and robust optimization.
In T. Gal and H. Greenberg, editors, Advances in Sensitivity
Analysis and Parametric Programming, chapter 12. Kluwer Academic Publishers,
1997.
Hercules Vladimirou and Stavros A. Zenios.
Scalable parallel computations for large-scale stochastic
programming.
Annals of Operations Research, 90:87-129, 1999.
Hercules Vladimirou and Stavros A. Zenios.
Stochastic programming: Parallel factorization of structured
matrices.
In Encyclopedia of Optimization, Vol. V., pages 338-343.
Kluwer Academic Publishers, 2001.
Silvia Vogel.
On stability in multiobjective programming-a stochastic approach.
Math. Programming, 56(1, Ser. A):91-119, 1992.
Silvia Vogel.
Stochastic stability concepts.
In Operations research proceedings 1990, Pap. 19th Annu. Meet.
DGOR, Vienna/Austria 1990, 57-64, 1992.
Silvia Vogel.
A stochastic approach to stability in stochastic programming.
J. Comput. Appl. Math., 56(1-2):65-96, 1994.
Stochastic programming: stability, numerical methods and applications
(Gosen, 1992).
Silvia Vogel.
Random approximations in multiobjective programming-with an
application to portfolio optimization with shortfall constraints.
Control Cybernet., 28(4):703-724, 1999.
Portfolio optimization (Laxenburg, 1998).
Silvia Vogel.
Stochastic programming and statistical estimates.
In Operations Research Proceedings 2003, pages 443-450,
Berlin, 2004. Springer.
Silvia Vogel.
Qualitative stability of stochastic programs with applications in
asymptotic statistics.
Statist. Decisions, 23(3):219-248, 2005.
Yu. M. Volin and G.M. Ostrovskii.
Optimization of technological processes under partial uncertainty
about the initial information.
Avtomat. i Telemekh., 12:85-98, 1995.
V.A. Volkonskii and S.A. Ivankov.
Saetze ueber die Konvergenz iterativer Prozeduren.
In Mat. Metody Resen. ekonom. Zadac 3, 37-51, 1972.
Y. V. Volkov and S. K. Zavriev.
A general stochastic outer approximations method.
SIAM J. Control Optim., 35(4):1387-1421, 1997.
V.L. Volkovic and E.P. Lavrinenko.
Das Problem der Kompromissregelung in hierarchischen Systemen mit
zwei Ebenen unter Beruecksichtigung zufaelliger Stoerungen.
Avtomat. vycislit. Tehn., Riga 1973, Nr. 2, 48-55, 1973.
Konstantin Volosov, Gautam Mitra, Fabio Spagnolo, and Cormac Lucas.
Treasury management model with foreign exchange exposure.
Stochastic Programming E-Print Series, http://www.speps.org,
2004.
E.I. Volynskii, L.I. Poteskina, and G.V. Filatov.
Die Verwendung einiger Glaettungs-Operatoren in
Extremum-Such-Problemen.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1975, Nr. 2, 90-95, 1975.
H. Voogd.
Stochastic multicriteria evaluation by means of geometric scaling.
Delft Prog. Rep. 5, 112-125, 1980.
A.N. Voronin.
A stochastic problem of multicriterial optimization.
Soviet Automat. Control, 17(4):67-69 (1985), 1984.
M.A. Vorontsov, G.W. Carhart, M. Cohen, and G. Cauwenberghs.
Adaptive optics based on analog parallel stochastic optimization:
Analysis and experimental demonstration.
Journal of the Optical Society of America A, 17:1440-1453,
2000.
S.V. Vorozheeva, O.I. Chernykh, and G.D. Chernyshova.
Construction of probabilistic algorithms for optimization by means of
a modified Lagrange function for computer-aided design systems problems.
In Optimization and modeling in automated systems (Russian),
pages 87-90. Voronezh. Politekhn. Inst., Voronezh, 1988.
Michael D. Vose and Jonathan E. Rowe.
Random heuristic search: applications to GAs and functions of
unitation.
Comput. Methods Appl. Mech. Engrg., 186(2-4):195-220, 2000.
Michael D. Vose and Jonathan E. Rowe.
Random heuristic search: applications to GAs and functions of
unitation.
Comput. Methods Appl. Mech. Engrg., 186(2-4):195-220, 2000.
E.N. Voyevudskiy.
Stochastic optimisation of queueing system control.
In Optimization, Vol. 1, 2 (Singapore, 1992), pages 653-656.
World Sci. Publishing, River Edge, NJ, 1992.
Miroslav Voznyakovski and Danuta Zakzhevska.
Optimal strategy for testing the fulfillment of constraints in
iterative algorithms for searching for the extremum of a function.
Zeszyty Nauk. Politech. Lódz. Mat., 17:87-94, 1984.
M. N. Vyalyi.
Local optimization algorithms and the geometry of polyhedra.
In Combinatorial models and methods (Russian), pages 15-26.
Ross. Akad. Nauk Vychisl. Tsentr, Moscow, 1995.
M. N. Vyalyi.
On estimates for the values of a functional in polyhedra of the
subgraph of least weight problem.
In Combinatorial models and methods (Russian), pages 27-43.
Ross. Akad. Nauk Vychisl. Tsentr, Moscow, 1995.
F.de Vylder and M. Goovaerts.
Maximization of the variance of a stop-loss reinsured risk.
Insur. Math. Econ. 2, 75-80, 1983.
Jan Wachowiak.
On a problem of abstract stochastic optimization with
nondifferentiable cost function.
Funct. Approximatio Comment. Math., 5:106-112, 1977.
Janet M. Wagner and Oded Berman.
Models for planning capacity expansion of convenience stores under
uncertain demand and the value of information.
Ann. Oper. Res., 59:19-44, 1995.
Models for planning under uncertainty.
Janet M. Wagner, Uri Shamir, and David H. Marks.
Containing groundwater contamination: Planning models using
stochastic programming with recourse.
Eur. J. Oper. Res. 77, No.1, 1-26, 1994.
Kazumasa Wakimoto.
Algorithms for generating a random vector with restricted integer
components and their extension to matrix.
In Essays in probability and statistics, pages 179-188. Shinko
Tsusho, Tokyo, 1976.
Kazuyoshi Wakuta.
A method of successive approximations in continuous time Markovian
decision processes.
Res. Rep. Nagaoka Techn. College 12, 67-71, 1976.
H. Walk.
Zur Konvergenzgeschwindigkeit eines rekursiven
Penalisationsverfahrens der stochastischen Optimierung.
Österreich. Akad. Wiss. Math.-Natur. Kl. Sitzungsber. II,
198(8-10):381-399, 1989.
D. Walkup and R.J-B. Wets.
Stochastic programs with recourse.
SIAM Journal on Applied Mathematics, 15:1299-1314, 1967.
D.W. Walkup and R.J.-B. Wets.
Some practical regularity conditions for nonlinear programms.
SIAM J. Control 7, 430-436, 1969.
D.W. Walkup and R.J.B. Wets.
Stochastic programs with recourse: special forms.
In Proceedings of the Princeton Symposium on Mathematical
Programming (Princeton Univ., 1967), pages 139-161, Princeton, N.J., 1970.
Princeton Univ. Press.
J.van der Wall and J. Wessels.
Markov decision processes.
Stat. Neerl. 39, 219-233, 1985.
S. W. Wallace, editor.
Modelling: in memory of Åsa Hallefjord.
Baltzer Science Publishers BV, Bussum, 1998.
Ann. Oper. Res. 82 (1998).
Stein W. Wallace.
Solving stochastic programs with network recourse.
Networks, 16(3):295-317, 1986.
Stein W. Wallace.
A piecewise linear upper bound on the network recourse function.
Math. Program. 38, 133-146, 1987.
Stein W. Wallace.
Investing in arcs in a network to maximize the expected max flow.
Networks 17, No.1, 87-103, 1987.
Stein W. Wallace.
Bounds on the expected maximal value of a class of stochastic
network problems.
INFOR 26, No.3, 153-162, 1988.
Stein W. Wallace and Thorkell Helgason.
Structural properties of the progressive hedging algorithm.
Ann. Oper. Res., 31(1-4):445-455, 1991.
Stochastic programming, Part II (Ann Arbor, MI, 1989).
Stein W. Wallace and Roger J.-B. Wets.
Preprocessing in stochastic programming: the case of linear programs.
ORSA J. Comput., 4(1):45-59, 1992.
Stein W. Wallace and Roger J.-B. Wets.
Preprocessing in stochastic programming: the case of capacitated
networks.
ORSA J. Comput., 7(1):44-62, 1995.
Stein W. Wallace and Tie Cheng Yan.
Bounding multi-stage stochastic programs from above.
Math. Programming, 61(1, Ser. A):111-129, 1993.
Stein W. Wallace and William T. Ziemba, editors.
Applications of Stochastic Programming, volume 5 of
MPS-SIAM Book Series on Optimization.
SIAM, 2005.
S.W. Wallace.
A two-stage stochastic facility-location problem with time-dependent
supply.
In Numerical techniques for stochastic optimization, Springer
Ser. Comput. Math. 10, 489-513, 1988.
S.W. Wallace.
Decision making under uncertainty: Is sensitivity analysis of any
use?
Operations Research, 48:20-25, 2000.
S.W. Wallace, J. Higle, and S. Sen, editors.
Stochastic programming, algorithms and models.
Baltzer Science Publishers BV, Amsterdam, 1996.
Papers from the IFIP Workshop held in Lillehammer, January 1994, Ann.
Oper. Res. 64 (1996).
S. Travis Waller and Athanasios K. Ziliaskopoulos.
On the online shortest path problem with limited arc cost
dependencies.
Networks, 40(4):216-227, 2002.
Yieh Hei Wan.
On the average speed of Lemke's algorithm for quadratic
programming.
Math. Programming, 35(2):236-246, 1986.
Zhong Ping Wan.
An approximate-exact penalty function method for single stage
stochastic programming.
Gongcheng Shuxue Xuebao, 13(4):37-42, 1996.
Guangyuan Wang and Zhong Qiao.
Linear programming with fuzzy random variable coefficients.
Fuzzy Sets Syst. 57, No.3, 295-311, 1993.
Guangyuan Wang and Wenquan Wang.
Generalized fuzzy constraint programming.
Fuzzy Math. 6, No.1, 1-8, 1986.
I.-J. Wang and E.K.P. Chong.
A deterministic analysis of simultaneous perturbation stochastic
approximation.
In Proceedings of the 30th Conference on Information Sciences
and Systems, pages 918-922, 1996.
I.-J. Wang and E.K.P. Chong.
A deterministic analysis of stochastic approximation with randomized
directions.
IEEE Transactions on Automatic Control, 43:1749-1749, 1998.
I.-J. Wang and J.C. Spall.
A constrained simultaneous perturbation stochastic approximation
algorithm based on penalty functions.
In Proceedings of the American Control Conference, pages
393-399, 1999.
J. Wang.
Continuity of the feasible solution sets of probabilistic constrained
programs.
J. Optim. Theory Appl., 63(1):79-89, 1989.
J. Wang.
Differentiability of the constraint functions in probabilistic
constrained programs.
Optimization, 20(4):509-515, 1989.
Jin De Wang.
An introduction to stochastic programming.
Chinese J. Oper. Res., 3(1):22-27, 1984.
Jin De Wang.
Distribution sensitivity analysis for stochastic programs with
complete recourse.
Math. Programming, 31(3):286-297, 1985.
Jin De Wang.
An approximate method for solving chance-constrained programming
problems.
Chinese J. Oper. Res., 5(2):67-68, 1986.
Jin De Wang.
Lipschitz continuity of objective functions in stochastic programs
with fixed recourse and its applications.
Math. Programming Stud., 27:145-152, 1986.
Stochastic programming 84. I.
Jin De Wang.
Stability of stochastic programming with probabilistic constraints.
Nanjing Daxue Xuebao Shuxue Bannian Kan, 3(2):177-185, 1986.
Jin De Wang.
The sufficient optimality condition for a class of mathematical
programs and its application to stochastic programming.
Acta Math. Appl. Sinica, 9(2):138-145, 1986.
Jin De Wang.
Stability of the optimum of stochastic linear programs.
Nanjing Daxue Xuebao Shuxue Bannian Kan, 4(2):109-116, 1987.
Jin De Wang.
Approximate nonlinear programming algorithms for solving stochastic
programs with recourse.
Ann. Oper. Res., 31(1-4):371-384, 1991.
Stochastic programming, Part II (Ann Arbor, MI, 1989).
Jin De Wang.
Differential stability of the objective functions of stochastic
linear programs.
Acta Math. Appl. Sinica, 14(4):484-490, 1991.
Jin De Wang.
Stability in distribution problems of nonlinear stochastic
programming.
Asia-Pacific J. Oper. Res., 8(2):128-134, 1991.
Jin De Wang.
Stability of multistage stochastic programming.
Ann. Oper. Res., 56:313-322, 1995.
Stochastic programming (Udine, 1992).
Jin De Wang.
A successive approximation method for solving probabilistic
constrained programs.
Acta Math. Appl. Sinica (English Ser.), 11(1):51-58, 1995.
Jinde Wang.
Stochastic Programming.
Chinese Presses, Beijing, 1990.
(in Chinese).
Jinde Wang.
Statistical inference for stochastic programming.
Pac. J. Optim., 1(1):179-190, 2005.
L. Wang and J. Wang.
Limit distribution of statistical estimators for stochastic programs
with dependent samples.
ZAMM Z. Angew. Math. Mech., 79(4):257-266, 1999.
Lihong Wang.
Asymptotics of statistical estimates in stochastic programming
problems with long-range dependent samples.
Math. Methods Oper. Res., 55(1):37-54, 2002.
Ling Wang and Liang Zhang.
Stochastic optimization using simulated annealing with hypothesis
test.
Appl. Math. Comput., 174(2):1329-1342, 2006.
P. Patrick Wang and Der-San Chen.
Continuous optimization by a variant of simulated annealing.
Comput. Optim. Appl., 6(1):59-71, 1996.
Qian Wang, Rajan Batta, and Christopher M. Rump.
Algorithms for a facility location problem with stochastic customer
demand and immobile servers.
Ann. Oper. Res., 111:17-34, 2002.
Recent developments in the theory and applications of location
models, Part II.
S.-M. Wang, J.-C. Chen, H.-M. Wee, and K.-J. Wang.
Non-linear stochastic optimization using genetic algorithm for
portfolio selection.
Int. J. Oper. Res. (Taichung), 3(1):16-22, 2006.
Ying Ming Wang and Guo Wei Fu.
Generalized least deviation methods for deciding priority of
comparison matrices in the analytic hierarchy process.
J. Tsinghua Univ., 33(3):10-17, 1993.
Y. Wardi.
A stochastic algorithm using one sample point per iteration and
diminishing stepsizes.
J. Optim. Theory Appl., 61(3):473-485, 1989.
Y. Wardi.
On a proof of a Robbins-Monro algorithm. Addendum to: `A
stochastic algorithm using one sample point per iteration and diminishing
stepsizes' [J. Optim. Theory Appl. 61 (1989), no.
J. Optim. Theory Appl., 64(1):217, 1990.
Y. Wardi.
Stochastic algorithms with Armijo stepsizes for minimization of
functions.
J. Optim. Theory Appl., 64(2):399-417, 1990.
Y. Wardi.
Stochastic approximation algorithm for minimax problems.
J. Optim. Theory Appl., 64(3):615-640, 1990.
Manfred K. Warmuth and Dima Kuzmin.
Online variance minimization.
In Learning theory, volume 4005 of Lecture Notes in
Comput. Sci., pages 514-528. Springer, Berlin, 2006.
Tsunemi Watanabe and Hugh Ellis.
Robustness in stochastic programming models.
Appl. Math. Modelling, 17(10):547-554, 1993.
Tsunemi Watanabe and Hugh Ellis.
A joint chance-constrained programming model with row dependence.
Eur. J. Oper. Res. 77, No.2, 325-343, 1994.
D.W. Watkins, Jr., D.C. McKinney, and D.P. Morton.
Groundwater pollution control.
In S.W. Wallace and W.T. Ziemba, editors, Applications of
Stochastic Programming, pages 409-424. MPS-SIAM Series on Optimization,
2005.
D.W. Watkins, Jr., D.P. Morton, and D.C. McKinney.
Monte carlo techniques for estimating solution quality in stochastic
groundwater management models.
In Proceedings of the XII International Conference on
Computational Methods in Water Resources, Crete, pages 67-74, 1998.
R.R. Weber.
Optimal search for a randomly moving object.
J. Appl. Probab., 23(3):708-717, 1986.
T.M. Webster.
Some experience with stochastic approximation algorithms in
large-scale systems.
In Proceedings of the American Statistical Association,
Statistical Computing Section, pages 181-186, 1988.
Zeng Xin Wei and Jiang Tao Mo.
A new numerical method for stochastic programming with recourse.
Chinese Ann. Math. Ser. A, 23(5):601-610, 2002.
Andrés Weintraub and Jorge Vera.
A cutting plane approach for chance constrained linear programs.
Oper. Res., 39(5):776-785, 1991.
Joel Weisman and A.G. Holzman.
Engineering design optimization under risk.
Management Sci., Theory 19, 235-249, 1972.
Gideon Weiss.
Stochastic bounds on distributions of optimal value functions with
applications to PERT, network flows and realiability.
Oper. Res. 34, 595-605, 1986.
M. Werner.
Bemerkungen zur zweistufigen stochastischen Programmierung bei
Unsicherheit ueber die Verteilung der Zufallsvariablen.
In Operations Res.-Verf. 15, 171-173, 1973.
M. Werner.
Ein Lösungsansatz für ein spezielles zweistufiges
stochastisches Optimierungsproblem.
Z. Operations Res. Ser. A-B, 17(3):A119-A128, 1973.
Michael Werner.
Stochastische lineare Optimierungsmodelle.
Akademische Verlagsgesellschaft, Frankfurt am Main, 1973.
Studienbuch für Studierende der Mathematik, der Wirtschafts- und
Sozialwissenschaften sowie aller Ingenieur- und Naturwissenschaften ab 3.
oder 4. Semester, Mit einem Vorwort von Hans Hermann Weber.
R. Wernsdorf.
Anwendungen der linearen parametrischen Optimierung unter
Beruecksichtigung rechentechnischer Aspekte.
Wiss. Z. Tech. Hochsch. Ilmenau 28, No.5, 179-188, 1982.
Ralph Wernsdorf.
On the connectedness of the set of efficient points in convex
optimization problems with multiple or random objectives.
Math. Operationsforsch. Stat., Ser. Optimization 15, 379-387,
1984.
R. Wets.
Stochastic programming: solution techniques and approximation.
In Mathematical programming: the state of the art (Bonn, 1982),
pages 566-603. Springer, Berlin, 1983.
R. Wets and Yu. M. Ermol'ev.
Problems in stochastic optimization.
In Systems research (Russian), pages 45-61, Moscow, 1988.
"Nauka".
R. J.-B. Wets and W. T. Ziemba, editors.
Stochastic programming. State of the art. 1998.
Baltzer Science Publishers BV, Bussum, 1999.
Papers from the 8th International Conference on Stochastic
Programming held at the University of British Columbia, Vancouver, BC, August
8-16, 1998, Ann. Oper. Res. 85 (1999).
R.J-B. Wets.
Programming under uncertainty: The complete problem.
Z. Wahrscheinlichkeitstheorie und verw. Gebiete, 4:316-339,
1966.
R.J.-B. Wets.
Large scale linear programming techniques.
In Numerical techniques for stochastic optimization, volume 10
of Springer Ser. Comput. Math., pages 65-93. Springer, Berlin, 1988.
R.J-B. Wets.
Laws of large numbers for random lsc functions.
Applied Stochastic Analysis and Stochastics Monographs,
5:101-120, 1991.
R.J.-B. Wets.
Challenges in stochastic programming.
Mathematical Programming, 75(2):115-135, 1996.
Roger Wets.
Characterization theorems for stochastic programs.
Math. Programming, 2:166-175, 1972.
Roger Wets.
Stochastic programs with recourse: A basic theorem for multistage
problems.
Z. Wahrscheinlichkeitstheorie und Verw. Gebiete, 21:201-206,
1972.
Roger Wets.
Duality relations in stochastic programming.
In Symposia Mathematica, Vol. XIX (Convegno sulla Programmazione
Matematica e sue Applicazioni, INDAM, Rome, 1974), pages 341-355, London,
1976. Academic Press.
Roger J.-B. Wets.
Stochastic programs with fixed recourse: the equivalent deterministic
program.
SIAM Rev., 16:309-339, 1974.
Roger J.-B. Wets.
On the relation between stochastic and deterministic optimization.
In Control Theory, numer. Meth., Computer Syst. Mod.; internat.
Symp. Rocquencourt 1974, Lecture Notes Econ. math. Syst. 107, 350-361,
1975.
Roger J.-B. Wets.
The distribution problem and its relation to other problems in
stochastic programming.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 245-262. Academic Press, London, 1980.
Roger J.-B. Wets.
Stochastic multipliers, induced feasibility and nonanticipativity in
stochastic programming.
In Stochastic programming (Proc. Internat. Conf., Univ. Oxford,
Oxford, 1974), pages 137-146, London, 1980. Academic Press.
Roger J.-B. Wets.
Solving stochastic programs with simple recourse.
Stochastics, 10(3-4):219-242, 1983.
Roger J.-B. Wets.
Modeling and solution strategies for unconstrained stochastic
optimization problems.
In Stochastics and optimization, Sel. Pap. ISSO Meet.,
Gorguano/Italy 1982, Ann. Oper. Res. 1, 3-22, 1984.
Roger J.-B. Wets.
On a compactness theorem for epiconvergent sequences of functions.
In Mathematical programming (Rio de Janeiro, 1981), pages
347-355. North-Holland, Amsterdam, 1984.
Roger J.-B. Wets.
Algorithmic procedures for stochastic optimization.
In Computational mathematical programming (Bad Windsheim,
1984), volume 15 of NATO Adv. Sci. Inst. Ser. F Comput. Systems Sci.,
pages 309-322. Springer, Berlin, 1985.
Roger J.-B. Wets.
The aggregation principle in scenario analysis and stochastic
optimization.
In Algorithms and model formulations in mathematical programming
(Bergen, 1987), volume 51 of NATO Adv. Sci. Inst. Ser. F: Comput.
Systems Sci., pages 91-113, Berlin, 1989. Springer.
Roger J.-B. Wets.
Stochastic programming.
In Optimization, volume 1 of Handbooks Oper. Res.
Management Sci., pages 573-629. North-Holland, Amsterdam, 1989.
Roger J.-B. Wets.
Constrained estimation: consistency and asymptotics.
Appl. Stochastic Models Data Anal., 7(1):17-32, 1991.
Roger J.-B. Wets.
Stochastic programs with chance constraints: generalized convexity
and approximation issues.
In Generalized convexity, generalized monotonicity: recent
results (Luminy, 1996), pages 61-74. Kluwer Acad. Publ., Dordrecht, 1998.
Roger J.-B. Wets.
Statistical estimation from an optimization viewpoint.
Ann. Oper. Res., 85:79-101, 1999.
Stochastic programming. State of the art, 1998 (Vancouver, BC).
A. Whinston.
Decentralizability of a stochastic optimization approach to water
resource allocation.
In Inventory Control Water Storage, Conf., Gyoer 1971, Colloqu.
math. Soc. Janos Bolyai 7, 335-350, 1973.
Chelsea C. White.
Cost equality and inequality results for a partially observed
stochastic optimization problem.
IEEE Trans. Systems Man Cybernetics SMC-5, 576-582, 1975.
Chelsea C. White.
Procedures for the solution of a finite-horizon, partially observed,
semi- Markov optimization problem.
Operations Res. 24, 348-358, 1976.
D.J. White.
A min-max-max-min approach to solving a stochastic programming
problem with simple recourse.
Manage. Sci. 38, No.4, 540-554, 1992.
Charles H. Whiteman.
Analytical policy design under rational expectations.
Econometrica, 54(6):1387-1405, 1986.
Mary Whiteside, Mark Eakin, Byong Choi, and Henry D. Crockett.
Modified chance constrained linear programming under uncertainty.
J. Statist. Comput. Simulation, 67(3):255-287, 2000.
J.E. Whitney, K. Duncan, M. Richardson, and I. Bankman.
Parameter estimation in a highly nonlinear model using Simultaneous
Perturbation Stochastic Approximation.
Communications in Statistics - Theory and Methods,
29:1247-1256, 2000.
Peter Whittle.
Optimization over time., volume Volume I: Dynamic programming
and stochastic control. of Wiley Series in Probability and Mathematical
Statistics: Applied Probability and Statistics.
Chichester etc.: John Wiley & Sons., 1982.
R. Wiebking.
Stochastische Modelle zur optimalen Lastverteilung in einem
Kraftwerksverbund.
Z. Operat. Res., Ser. B 21, B197-B217, 1977.
Rolf D. Wiebking.
Optimal engineering design under uncertainty by geometric
programming.
Management Sci. 23, 644-651, 1977.
Robert Wieczorkowski.
Stochastic algorithms in discrete optimization with noisy values for
the function.
Mat. Stos., 38:119-153, 1995.
S.T. Wierzchon.
Linear programming with fuzzy sets: A general approach.
Math. Modelling 9, 447-459, 1987.
S.T. Wierzchon.
Randomness and fuzziness in a linear programming problem.
In Combining, fuzzy imprecision with probabilistic uncertainty
in decision making, Lect. Notes Econ. Math. Syst. 310, 227-239, 1988.
A.G. Wilson.
Linear programming and entropy maximizing models.
Research memorandum. rm-74-6., Laxenburg, Austria: IIASA -
International Institute for Applied Systems Analysis., 1974.
Dan Wilson.
A mean cost approximation for transportation problems with
stochastic demand.
Naval Res. Logist. Quart. 22, 181-187, 1975.
William E. Winkler.
On Dykstra's iterative fitting procedure.
Ann. Probab., 18(3):1410-1415, 1990.
Wayne L. Winston.
Operations research applications and algorithms. 2nd. ed.
Thomson Information/Publishing Group, Boston., 1991.
P. Witten and H.-G. Zimmermann.
Stochastische Transportprobleme.
Z. Operations Res. Ser. A-B, 22(1):A55-A68, 1978.
H. Wolf.
Die Ermittlung effizienter Lösungen zur stochastischen linearen
Optimierungsaufgabe.
OR Spektrum, 7(2):81-90, 1985.
Hartmut Wolf.
Entscheidungsfindung bei der stochastischen linearen
Optimierung durch Entscheidungsmodelle mit mehrfacher Zielsetzung,
volume 84 of Mathematical Systems in Economics.
Verlagsgruppe Athenäum/Hain/Hanstein, Königstein/Ts., 1983.
Richard D. Wollmer.
Investment in stochastic minimum cost generalized multicommodity
networks with application to coal transport.
Networks, 10(4):351-362, 1980.
Richard D. Wollmer.
Two-stage linear programming under uncertainty with 0-1 integer
first stage variables.
Math. Programming, 19(3):279-288, 1980.
Richard D. Wollmer.
Critical path planning under uncertainty.
Math. Program. Study 25, 164-171, 1985.
Richard D. Wollmer.
Investments in stochastic maximum flow networks.
Ann. Oper. Res., 31(1-4):459-467, 1991.
Stochastic programming, Part II (Ann Arbor, MI, 1989).
David H. Wolpert, Charlie E. M. Strauss, and Dev Rajnarayan.
Advances in distributed optimization using probability collectives.
Adv. Complex Syst., 9(4):383-436, 2006.
Eugene Wong.
Neural computing and stochastic optimization.
In Future tendencies in computer science, control and applied
mathematics (Paris, 1992), volume 653 of Lecture Notes in Comput.
Sci., pages 339-342. Springer, Berlin, 1992.
G. R. Wood, D. L. J. Alexander, and D. W. Bulger.
Approximation of the distribution of convergence times for stochastic
global optimisation.
J. Global Optim., 22(1-4):271-284, 2002.
Dedicated to Professor Reiner Horst on his 60th birthday.
G. R. Wood, Z. B. Zabinsky, and B. P. Kristinsdottir.
Hesitant adaptive search: the distribution of the number of
iterations to convergence.
Math. Program., 89(3, Ser. A):479-486, 2001.
G.R. Wood.
On computing the dispersion function.
J. Optim. Theory Appl., 58(2):331-350, 1988.
G.R. Wood and B.P. Zhang.
Estimation of the Lipschitz constant of a function.
J. Global Optim., 8(1):91-103, 1996.
Graham R. Wood and Zelda B. Zabinsky.
Stochastic adaptive search.
In Handbook of global optimization, Vol. 2, volume 62 of
Nonconvex Optim. Appl., pages 231-249. Kluwer Acad. Publ., Dordrecht, 2002.
David L. Woodruff, editor.
Advances in computational and stochastic optimization, logic
programming, and heuristic search.
Kluwer Academic Publishers, Boston, MA, 1998.
Interfaces in computer science and operations research.
S.E. Wright.
Primal-dual aggregation and disaggregation for stochastic linear
programs.
Math. Oper. Res., 19(4):893-908, 1994.
S.J. Wright and J.N. Holt.
Algorithms for nonlinear least squares with linear inequality
constraints.
SIAM J. Sci. Statist. Comput., 6(4):1033-1048, 1985.
Dong Hua Wu, Wei Wen Tian, Wei Huang, and Dao De Gao.
A modified integral level-set method for solving global optimization.
J. Shanghai Univ. Nat. Sci., 7(3):221-224, 2001.
Jason Wu and Suvrajeet Sen.
A stochastic programming model for currency option hedging.
Ann. Oper. Res., 100:227-249 (2001), 2000.
Research in stochastic programming (Vancouver, BC, 1998).
Wei Wu and Yuesheng Xu.
Deterministic convergence of an online gradient method for neural
networks.
J. Comput. Appl. Math., 144(1-2):335-347, 2002.
Zhi You Wu.
An approximate computational method for single stage stochastic
programming.
J. Chongqing Norm. Univ. Nat. Sci. Ed., 17(1):23-28, 2000.
Henry P. Wynn and Anatoly A. Zhigljavsky.
The theory of search from a statistical viewpoint.
Test, 3(2):1-45, 1994.
With discussion and a reply by the authors.
You Min Xi and Lei Yang.
Probability aggregation in rational group decision-making.
Xi'an Jiaotong Daxue Xuebao, 31(suppl. I):110-114, 1997.
Xiaolan Xie.
Evaluation and optimization of two-stage continuous transfer lines
subject to time-dependent failures.
Discrete Event Dyn. Syst., 12(1):109-122, 2002.
WODES '98 (Cagliari).
Cheng Xian Xu and Zhi Ping Chen.
A dual gradient method for solving a class of two-stage stochastic
programming problems with recourse.
Xi 'an Jiaotong Daxue Xuebao, 26(2):123-125, 1992.
Chengxian Xu and Zhiping Chen.
A nonlinear model of multistage problem with recourse.
Math. Appl., 9(3):358-363, 1996.
H. Xu.
Stochastic penalty function methods for nonsmooth constrained
minimization.
J. Optim. Theory Appl., 88(3):709-724, 1996.
Huifu Xu.
An implicit programming approach for a class of stochastic
mathematical programs with complementarity constraints.
SIAM J. Optim., 16(3):670-696 (electronic), 2006.
Jiu Ping Xu and Jun Li.
An interactive algorithm for a class of stochastic multi-objective
linear-quadratic programming models.
J. Systems Sci. Math. Sci., 22(1):96-106, 2002.
Jiuping Xu and Jun Li.
A class of stochastic optimization problems with one quadratic &
several linear objective functions and extended portfolio selection model.
J. Comput. Appl. Math., 146(1):99-113, 2002.
Sino-Japan Optimization Meeting (Hong Kong, 2000).
Li Jian Xu, Bai Wu Wan, and Chong Zhao Han.
Robustness of a class of stochastic steady-state optimization
algorithms.
Xi'an Jiaotong Daxue Xuebao, 29(8):17-23, 31, 1995.
Diana Yakowitz.
An exact penalty algorithm for recourse-constrained stochastic linear
programs.
Appl. Math. Comput., 49(1):39-62, 1992.
Diana S. Yakowitz.
A regularized stochastic decomposition algorithm for two-stage
stochastic linear programs.
Comput. Optim. Appl., 3(1):59-81, 1994.
S. Yakowitz and E. Lugosi.
Random search in the presence of noise, with application to machine
learning.
SIAM J. Sci. Stat. Comput. 11, No.4, 702-712, 1990.
Sid Yakowitz.
A decision model and methodology for the AIDS epidemic.
Appl. Math. Comput. 52, No.1, 149-172, 1992.
Sid Yakowitz.
A globally convergent stochastic approximation.
SIAM J. Control Optim., 31(1):30-40, 1993.
Sid Yakowitz, Thusitha Jayawardena, and Shu Li.
Theory for automatic learning under partially observed
Markov-dependent noise.
IEEE Trans. Automat. Control, 37(9):1316-1324, 1992.
Sidney Yakowitz, Pierre L'Ecuyer, and Felisa Vázquez-Abad.
Global stochastic optimization with low-dispersion point sets.
Oper. Res., 48(6):939-950, 2000.
S.J. Yakowitz and Lloyd Fisher.
On sequential search for the maximum of an unknown function.
J. Math. Anal. Appl., 41:234-259, 1973.
D. Yan and H. Mukai.
Optimization algorithm with probabilistic estimation.
J. Optim. Theory Appl., 79(2):345-371, 1993.
Di Yan and H. Mukai.
Discrete optimization with estimation.
In Proceedings of the 28th IEEE Conference on Decision and
Control, Vol. 1-3 (Tampa, FL, 1989), pages 2463-2468, New York, 1989.
IEEE.
Di Yan and H. Mukai.
Stochastic discrete optimization.
SIAM J. Control Optim., 30(3):594-612, 1992.
H. Yan, G. Yin, and S.X.-C. Lou.
Using stochastic optimization to determine threshold values for the
control of unreliable manufacturing systems.
J. Optim. Theory Appl., 83(3):511-539, 1994.
Tie Cheng Yan.
Several new convexity statements in stochastic programming.
Numer. Math. J. Chinese Univ., 6(2):181-185, 1984.
Tie Cheng Yan.
A fixed-point algorithm with a simple penalty matrix for solving
two-stage stochastic programming problems.
Numer. Math. J. Chinese Univ., 15(3):240-249, 1993.
Tie Cheng Yan.
A class of feasible policies in multistage stochastic programming.
J. Systems Engrg., 10(2):41-47, 1995.
Tiecheng Yan.
The locating method for solving two-stage simple recourse stochastic
programming.
Numer. Math., Nanjing 15, No.3, 240-249, 1993.
Dafeng Yang and Stavros A. Zenios.
A scalable parallel interior point algorithm for stochastic linear
programming and robust optimization.
Comput. Optim. Appl., 7(1):143-158, 1997.
Computational issues in high performance software for nonlinear
optimization (Capri, 1995).
M. S. Yang and L. H. Lee.
Ordinal optimization with subset selection rule.
J. Optim. Theory Appl., 113(3):597-620, 2002.
Mike S. Yang, Loohay Lee, and Yu-Chi Ho.
On stochastic optimization and its applications to manufacturing.
In Mathematics of stochastic manufacturing systems
(Williamsburg, VA, 1996), volume 33 of Lectures in Appl. Math., pages
317-331. Amer. Math. Soc., Providence, RI, 1997.
Ping Yang.
Least controls for a class of constrained linear stochastic systems.
Math. Oper. Res., 18(2):275-291, 1993.
Tae-Yong Yang and Hyun-Joon Kim.
A study on the extension of fuzzy programming solution method.
J. Korean Oper. Res. Manage. Sci. Soc. 11, No.1, 36-43, 1986.
Yaguang Yang.
A new approach to uncertain parameter linear programming.
Eur. J. Oper. Res. 54, No.1, 95-114, 1991.
Hitoshi Yano and Masatoshi Sakawa.
Interactive fuzzy decision making for generalized multiobjective
linear fractional programming problems with fuzzy parameters.
Fuzzy Sets and Systems, 32(3):245-261, 1989.
Makoto Yano.
Comparative statics in dynamic stochastic models. Differential
analysis of a stochastic modified golden rule state in a Banach space.
J. Math. Econ. 18, No.2, 169-185, 1989.
O.V. Yashkir.
The stochastic quasigradient method in problems of multifrequency
nonlinear interaction.
Issled. Operatsii i ASU, 23:26-29, 139, 1984.
O.V. Yashkir.
The method of stochastic quasigradients in multifrequency problems
on nonlinear interaction.
Issled. Oper. ASU 23, 26-29, 1984.
A.I. Yastremskii.
Stochastic production models. I.
Issled. Operatsii i ASU, 12:93-101, 133, 1978.
A.I. Yastremskii.
Stochastic production models. II. Marginal correlations.
Issled. Operatsii i ASU, 13:105-112, 136, 1979.
A.I. Yastremskii.
Stochastic production models. III. A dynamic stochastic
model.
Issled. Operatsii i ASU, 14:19-27, 133, 1979.
A.I. Yastremskii.
Marginal relations for the linear problem of multistage stochastic
programming.
Cybernetics, 16(2):297-299, 1980.
A.I. Yastremskii.
Optimality conditions in stochastic programming.
Cybernetics, 16(1):154-158, 1980.
A.I. Yastremskii.
Stochastic models of economics and methods of their qualitative
analysis.
In Mathematical methods for the solution of economic problems,
No. 9 (Russian), pages 175-185, Moscow, 1980. "Nauka".
A.I. Yastremskii.
On duality relations and optimality conditions in linear problems of
stochastic programming.
Kibernetika (Kiev), 1:102-107, 135, 1987.
A.I. Yastremskii.
On the dependence of the effectiveness of some stochastic systems on
the degree of dispersion of random parameters.
Issled. Operatsii i ASU, 35:21-29, 1991.
A.I. Yastremskij.
Stochastic models and methods of optimal planning.
In Stochastic optimization, Proc. Int. Conf., Kiev/USSR 1984,
Lect. Notes Control Inf. Sci. 81, 602-607, 1986.
A.I. Yastremskij and I.K. Fedorenko.
Techniques for solving and analyzing applied stochastic models in
economics.
Cybernetics 16, 137-144 translation from Kibernetika 1980, No.1,
122-127 (1980)., 1980.
A.I. Yastremskij and L.I. Krasnikova.
Some models of optimal distribution of resources in execution of a
complex of scientific and technological tasks.
Sov. J. Autom. Inf. Sci. 19, No.5, 80-83 translation from
Avtomatika 1986, No.5, 80-83 (1986)., 1986.
A.I. Yastremskij and M.V. Mikhalevich.
Stochastic search methods for a most preferable element and their
interactive interpretation.
Cybernetics 16, 893-898 translation from Kibernetika 1980, No.6,
90-94 (1980)., 1981.
A.I. Yastremskij and N. Uteuliev.
Modeling possibilities of two-step and multistep stochastic models.
Sov. J. Autom. Inf. Sci. 20, No.3, 70-73 translation from
Avtomatika 1987, No.3, 84-88 (1987)., 1987.
Yaudin, D.B.
Matematicheskie metody upravleniya v usloviyakh nepolnoi\
informatsii.
"Sovetskoe Radio", Moscow, 1974.
Zadachi i metody stokhasticheskogo programmirovaniya. [Problems
and methods of stochastic programming].
Y. Yavin.
Optimal launch conditions: an optimal stochastic control problem.
Comput. Math. Appl., 21(6-7):115-126, 1991.
A.V. Yazenin.
Fuzzy and stochastic programming.
Fuzzy Sets and Systems, 22(1-2):171-180, 1987.
A.V. Yazenin.
Linear programming with random fuzzy data.
Soviet J. Comput. Systems Sci., 30(3):86-93, 1992.
A.V. Yazenin.
Modeling of constraints in problems of possibilistic linear
programming.
J. Comput. Systems Sci. Internat., 33(4):71-75, 1995.
A.V. Yazenin.
On the problem of possibilistic optimization.
Fuzzy Sets and Systems, 81(1):133-140, 1996.
Fuzzy optimization.
H. Q. Ye and Z. B. Tang.
Partitioned random search for global optimization with sampling cost
and discounting factor.
J. Optim. Theory Appl., 110(2):445-455, 2001.
Uri Yechiali.
A note on a stochastic production-maximizing transportation
problem.
Naval Res. Logist. Quart. 18, 429-431, 1971.
James R. Yee and Bruce L. Golden.
A note on detemining operating strategies for probabilistic vehicle
routing.
Nav. Res. Logist. Q. 27, 159-163, 1980.
I.I. Yeremin and V.D. Mazurov.
Synthesis of nonstationary processes of mathematical programming,
containing pattern recognition.
In Studies on mathematical programming (Papers, Third Conf.
Math. Programming, Mátrafüred, 1975), volume 1 of Math. Methods
Oper. Res., pages 61-68, Budapest, 1980. Akad. Kiadó.
Yu. M. Yermol'yev.
A general stochastic programming problem.
J. Cybernet., 1(4):106-112, 1971.
G. Yin.
Stochastic approximation via averaging: The Polyak's approach
revisited.
In Simulation and optimization, Proc. Int. Workshop Comput.
Intensive Methods Simulation Optimization, Laxenburg/Austria 1990, Lect.
Notes Econ. Math. Syst. 374, 119-134, 1992.
G. Yin.
Rates of convergence for a class of global stochastic optimization
algorithms.
SIAM J. Optim., 10(1):99-120 (electronic), 1999.
G. Yin.
Convergence of a global stochastic optimization algorithm with
partial step size restarting.
Adv. in Appl. Probab., 32(2):480-498, 2000.
G. Yin and Ishita Gupta.
On a continuous time stochastic approximation problem.
Acta Appl. Math., 33(1):3-20, 1993.
Stochastic optimization.
G. Yin, R. H. Liu, and Q. Zhang.
Recursive algorithms for stock liquidation: a stochastic optimization
approach.
SIAM J. Optim., 13(1):240-263 (electronic), 2002.
G. Yin, H.M. Yan, and S.X.-C. Lou.
On a class of stochastic optimization algorithms with applications to
manufacturing models.
In Model-oriented data analysis (Petrodvorets, 1992), Contrib.
Statist., pages 213-226. Physica, Heidelberg, 1993.
G. Yin, Q. Zhang, H. M. Yan, and E. K. Boukas.
Random-direction optimization algorithms with applications to
threshold controls.
J. Optim. Theory Appl., 110(1):211-233, 2001.
George Yin, Günter Rudolph, and Hans-Paul Schwefel.
Establishing connections between evolutionary algorithms and
stochastic approximation.
Informatica, 6(1):93-117, 1995.
George Yin and Qing Zhang, editors.
Recent advances in control and optimization of manufacturing
systems, volume 214 of Lecture Notes in Control and Information
Sciences.
Springer-Verlag London Ltd., London, 1996.
Suk-Hun Yoon and Jose A. Ventura.
Minimizing the mean weighted absolute deviation from due dates in
lot-streaming flow shop scheduling.
Comput. Oper. Res., 29(10):1301-1315, 2002.
Yuji Yoshida.
Duality in dynamic fuzzy systems.
Fuzzy Sets and Systems, 95(1):53-65, 1998.
Yasunari Yoshitomi, Hiroko Ikenoue, Toshifumi Takeba, and Shigeyuki Tomita.
Genetic algorithm in uncertain environments for solving stochastic
programming problem.
J. Oper. Res. Soc. Japan, 43(2):266-290, 2000.
Zhao Yong You.
Convergence theorems of quasi-Picard iteration of a contraction
mapping on probabilistic metric spaces.
J. Math. Res. Exposition, 1:25-28, 1981.
Zhao Yong You, Cheng Xian Xu, and Zhi Ping Chen.
Measurability of optimal values and optimal solutions to nonlinear
stochastic programming problems.
Xi'an Jiaotong Daxue Xuebao, 27(3):113-119, 1993.
Zhao Yong You, Cheng Xian Xu, and Zhi Ping Chen.
A dual parallel algorithm for solving multistage stochastic
programming problems with recourse.
Numer. Math. J. Chinese Univ., 16(4):321-331, 1994.
Richard M. Young.
Certainty equivalence, first order certainty equivalence, stochastic
control, and the covariance structure.
Econometrica 43, 421-430, 1975.
Jingyuan Yu, Baozhu Guo, and Guangtian Zhu.
The control of the semi-discrete population evolution system.
J. Syst. Sci. Math. Sci. 7, 214-219, 1987.
A.D. Yudin.
Duality in multistage stochastic programming.
Engrg. Cybernetics, 11(6):908-915, 1973.
A.D. Yudin.
Descent along coordinates in infinite-dimensional convex programming
problems.
Engrg. Cybernetics, 12:29-38, 1974.
D.B. Yudin.
Mathematical Methods of Management under Incomplete
Information.
Problems and Methods of Stochastic Programming. Soviet Radio, Moscow,
1974.
(in Russian).
D.B. Yudin.
Problems and Methods of Stochastic Programming.
Soviet Radio, Moscow, 1979.
(in Russian).
D.B. Yudin.
Construction of decision rules in stochastic programming problems.
Avtomat. i Telemekh., 11:76-85, 1984.
D.B. Yudin and A.S. Nemirovskij.
The efficiency of the randomization of control.
Probl. Sluchajnogo Poiska 7, 22-66, 1978.
D.B. Yudin and E.V. Tzoy.
Integer stochastic programming.
Izvestia AN SSSR, Tekhnicheskaya Kibernetika, 1:3-11, 1974.
in Russian.
Hong Yue and Hong Wang.
Minimum entropy control of closed-loop tracking errors for dynamic
stochastic systems.
IEEE Trans. Automat. Control, 48(1):118-122, 2003.
Yan M. Yufik and Thomas B. Sheridan.
Hybrid knowledge-based decision aid for operators of large scale
systems.
Large Scale Syst. 10, 133-146, 1986.
A.A. Yushkevich.
Bellman inequalities for Markov decision drift processes.
In Probability theory and mathematical statistics, Vol. II
(Vilnius, 1985), pages 695-701. VNU Sci. Press, Utrecht, 1987.
A.A. Yushkevich.
Continuous-time Markov decision processes with interventions.
Stochastics, to appear.
Z.B. Zabinsky and B.P. Kristinsdottir.
Complexity analysis integrating pure adaptive search (PAS) and
pure random search (PRS).
In Developments in global optimization (Szeged, 1995),
volume 18 of Nonconvex Optim. Appl., pages 171-181. Kluwer Acad.
Publ., Dordrecht, 1997.
Zelda B. Zabinsky and Graham R. Wood.
Implementation of stochastic adaptive search with hit-and-run as a
generator.
In Handbook of global optimization, Vol. 2, volume 62 of
Nonconvex Optim. Appl., pages 251-273. Kluwer Acad. Publ., Dordrecht, 2002.
Jitka Zackova.
A note on deterministic equivalents to stochastic linear programming
problems.
Z. Wahrscheinlichkeitstheorie verw. Gebiete 14, 264-268, 1970.
Jitka Zácková.
A note on deterministic equivalents to stochastic linear programming
problems. II.
Z. Wahrscheinlichkeitstheorie und Verw. Gebiete, 24:221-227,
1972.
Yu.P. Zajchenko and S.A. Shumilova.
Operations research. Collection of problems. Textbook.
(Issledovanie operatsij. Sbornik zadach. Uchebnoe posobie).
Kiev: Izdatel'stvo pri Kievskom Gosudarstvennom Universitete
Izdatel'skogo Ob"edineniya "Vishcha Shkola"., 1984.
Ju. A. Zak.
An application of stochastic programming methods to the solution of
certain problems on the optimization of multivariable memoryless objects.
In Theory and methods of construction of multivariable control
systems (Russian), pages 170-183, 337. Izdat. "Nauka", Moscow, 1973.
Ju. A. Zak, G.P. Kondrasin, and A.P. Lenin.
A study of algorithms of random search in problems of the
optimization of complex systems.
In Problems of random search, 2 (Russian), pages 131-149, 156,
221. Izdat. "Zinatne", Riga, 1973.
Yu. A. Zak and V.N. Yakhno.
Sequential optimization algorithms in problems of discrete stochastic
programming.
Engrg. Cybernetics, 18(1):6-13 (1981), 1980.
Yu. O. Zak.
On some problems of optimization of continuous technological
processes.
Soviet Automat. Control, 1968(6):8-18, 1969.
Golbon Zakeri, Andrew B. Philpott, and David M. Ryan.
Inexact cuts in Benders decomposition.
SIAM J. Optim., 10(3):643-657 (electronic), 2000.
V.V. Zakharov.
Methode der Integralglaettung in multiextremalen und stochastischen
Problemen.
Izv. Akad. Nauk SSSR, Tekh. Kibern. 1970, No.4, 19-25, 1970.
M.M. Zakhvatkin and S.P. Uryas'ev.
A modification of the Arrow-Hurwicz algorithm for search for
saddle points.
Issled. Operatsii i ASU, 21:16-20, 1983.
L.Ya. Zamanskij and D.M. Giller.
Survivability analysis of stochastic graphs.
Issled. Oper. ASU 19, 27-30, 1982.
D.K. Zambitskij.
Parametric Steiner problem for discrete median metric spaces and its
application.
Izv. Akad. Nauk Mold. SSR, Ser. Fiz.-Tekh. Mat. Nauk 1989, No.1,
9-14, 1989.
Giovanni M. Zambruno.
Stochastic programming in portfolio selection.
Oper. Res. Verfahren 35, 577-579, 1979.
V.K. Zavadskii.
The use of simplified models in problems of numerical optimization
and solution of equations.
Avtomat. i Telemekh., 7:4-10, 1996.
S.K. Zavriev.
Hybrid penalty and stochastic gradient method for seeking a
max-min.
U.S.S.R. Comput. Math. Math. Phys. 19, No.2, 58-72 translation
from Zh. Vychisl. Mat. Mat. Fiz. 19, 329-342 (1979)., 1980.
S.K. Zavriev.
On finding stationary points in the problem of searching for a
maximin with constraints.
Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet.,
2:48-57, 73, 1980.
S.K. Zavriev.
A stochastic method of discrepancies for seeking max-min.
U.S.S.R. Comput. Math. Math. Phys. 21, No.3, 54-64 translation
from Zh. Vychisl. Mat. Mat. Fiz. 21, 585-594 (1981)., 1981.
S.K. Zavriev and A.G. Perevozchikov.
Attraction of trajectories of finite-difference inclusions and
stability of numerical methods of stochastic nonsmooth optimization.
Sov. Phys., Dokl. 35, No.8, 709-711 translation from Dokl. Akad.
Nauk SSSR 313, No.6, 1373-1376 (1990)., 1990.
S.K. Zavriev and A.G. Perevozchikov.
A stochastic quasigradient method for optimization of the parameters
of dynamical systems.
Kibernetika (Kiev), 4:104-107, 130, 134, 1990.
S.K. Zavriev and A.G. Perevozchikov.
The method of generalized stochastic gradient for solving minimax
problems with constrained variables.
U.S.S.R. Comput. Math. Math. Phys. 30, No.2, 98-105 translation
from Zh. Vychisl. Mat. Mat. Fiz. 30, No.4, 491-500 (1990)., 1990.
S.K. Zavriev and A.G. Perevozchikov.
On a formula for computing stochastic generalized gradients.
In Software and models of systems analysis (Russian), pages
173-178, 195. Moskov. Gos. Univ., Moscow, 1991.
S.K. Zavriev and A.G. Perevozchikov.
A stochastic analogue of the method of feasible directions.
Issled. Operatsii i ASU, 37:9-18, 1991.
S.K. Zavriev and A.G. Perevozchikov.
Stochastic generalized gradient methods in problems of the design of
dynamical systems.
Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet.,
4:47-52, 74, 1992.
M.K. Zavrieva.
A Combined Penalty and Stochastic Quasigradient Method for the
Search for a Connected Maximin.
Soobshcheniya po Prikladnoi Matematike. [Reports in Applied
Mathematics]. Akad. Nauk SSSR Vychisl. Tsentr, Moscow, 1989.
(in Russian).
A.I. Zdanok.
Relaxation of stochastic optimization algorithms in the absence of
noise.
Problemy Sluchain. Poiska, 7:106-122, 318, 1978.
Adaptation problems in technical systems.
A.I. Zdanok.
Relaxation of stochastic optimization algorithms in the presence of
noise.
Problemy Sluchain. Poiska, 7:123-134, 318, 1978.
Adaptation problems in technical systems.
Zeev Zeitlin.
Optimality conditions for the stochastic transportation problem.
Internat. J. Math. Ed. Sci. Tech., 14(1):9-14, 1983.
G. Zellmer.
Ueber die Stabilitaet der optimalen Loesung linearer
Optimierungsaufgaben mit stochastischen Daten.
In XV. Int. Wiss. Kolloquium Tech. Hochsch. Ilmenau 1970, Abt.
A, 3-8, 1970.
G. Zellmer.
Zum Zusammenhang zwischen Problemen mit
Wahrscheinlichkeitsbeschraenkungen und Problemen mit Kompensation in der
stochastischen linearen Optimierung.
In 21. int. wiss. Kolloq.; Ilmenau 1976, Heft 3, 27-29, 1976.
Eitan Zemel.
Random binary search: a randomizing algorithm for global optimization
in r\sp 1.
Math. Oper. Res., 11(4):651-662, 1986.
Stavros A. Zenios.
A model for portfolio management with mortgage-backed securities.
Ann. Oper. Res., 43(1-4):337-356, 1993.
Applied mathematical programming and modelling (Uxbridge, 1991).
V. Zenísek.
An optimum seeking procedure for stochastic problems with
restrictions.
Internat. J. Control (1), 15:513-521, 1972.
V.V. Zenkov and Ju.M. Codikov.
Ein Verfahren zur Loesung von Problemen der stochastischen
Programmierung.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1973, Nr. 3, 13-15, 1973.
V.V. Zenkov and Yu. M. Tsodikov.
A method of solving stochastic programming problems.
Engrg. Cybernetics, 11(3):369-371, 1973.
Mihail Zervos.
On the epiconvergence of stochastic optimization problems.
Math. Oper. Res., 24(2):495-508, 1999.
S.V. Zhak.
Semistochastische Methoden zur Loesung von Aufgaben der konvexen
Programmierung.
Kibernetika 1968, No.6, 32-37, 1968.
A.X. Zhang and Sy-Ming Guu.
Properties of the multiple lot-sizing problem with rigid demands and
general yield distributions.
Comput. Math. Appl., 33(5):55-65, 1997.
Joyce Li Zhang and K. Ponnambalam.
Hydro energy management optimization in a deregulated electricity
market.
Optim. Eng., 7(1):47-61, 2006.
Ke Bang Zhang and Bing Zhang Huang.
A probabilistic objective model for probabilistic constrained
programming and its solution.
J. Shanghai Jiaotong Univ., 26(1):90-95, 1992.
Ke Bang Zhang and Ying Zhang.
The structure and programming of a stochastic method for global
optimization.
J. Shanghai Jiaotong Univ., 4:80-86, 119, 1986.
Wei-Bin Zhang.
Existence of aperiodic time-dependent solutions in optimal growth
economy with three sectors.
Internat. J. Systems Sci., 20(10):1943-1953, 1989.
Gongyun Zhao.
A log-barrier method with Benders decomposition for solving
two-stage stochastic linear programs.
Math. Program., 90(3, Ser. A):507-536, 2001.
Gongyun Zhao.
A Lagrangian dual method with self-concordant barriers for
multi-stage stochastic convex programming.
Math. Program., 102(1, Ser. A):1-24, 2005.
Ji Chao Zhao.
Equilibrium strategies for sealed bids with a g(a,1)
probability distribution of the opponents' valuations.
Qufu Shifan Daxue Xuebao Ziran Kexue Ban, 21(5):123-130, 1995.
Jin Zhao.
Limit distributions for optimal solutions in stochastic programming.
Nanjing Daxue Xuebao Shuxue Bannian Kan, 14(1):154-160, 1997.
Tian Xu Zhao and Hong Tao Li.
Continuity of objective functions in stochastic programming.
J. Baoji College Arts Sci. Nat. Sci., 17(2):9-12, 1997.
Tian Xu Zhao and Xu Zi Tian.
Solving of a kind of stochastic programming by approximation.
J. Baoji College Arts Sci. Nat. Sci., 21(1):10-12, 2001.
Yonggan Zhao and William T. Ziemba.
Creating synthetic option strategies for asset allocation with
transaction costs using multi-period stochastic programming.
Stochastic Programming E-Print Series, http://www.speps.org,
1999.
Yonggan Zhao and William T. Ziemba.
Determining risk neutral probabilities and optimal portfolio policies
in a dynamic investment model with downside risk control in the presence of
trading frictions.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Yonggan Zhao and William T. Ziemba.
Mean-variance versus expected utility in dynamic investment analysis.
Stochastic Programming E-Print Series, http://www.speps.org,
2000.
Yonggan Zhao and William T. Ziemba.
A stochastic programming model using an endogenously determined worst
case risk measure for dynamic asset allocation.
Math. Program., 89(2, Ser. B):293-309, 2001.
Mathematical programming and finance.
Yonggan Zhao and William T. Ziemba.
Intertemporal mean-variance efficiency with a markovian state price
density.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
Yonggan Zhao and William T. Ziemba.
On leland's option hedging strategy with transaction costs.
Stochastic Programming E-Print Series, http://www.speps.org,
2003.
D. Zhargal and S.S. Lebedev.
Integer programming problems with imprecisely specified right-hand
sides.
Èkonom. i Mat. Metody, 24(3):518-527, 1988.
A.I. Zhdanok.
A relaxation of stochastic optimization algorithms under conditions
of noise.
Probl. Sluchajnogo Poiska 7, 123-134, 1978.
A.I. Zhdanok.
Algorithms of random sorting under conditions of noise.
Probl. Sluchajnogo Poiska 8, 149-161, 1980.
A.I. Zhdanov.
Solution of ill-posed stochastic linear algebraic equations by the
regularized maximum likelihood method.
Zh. Vychisl. Mat. i Mat. Fiz., 28(9):1420-1425, 1440, 1988.
Anatoly Zhigljavsky.
Branch and probability bound methods for global optimization.
Informatica, 1(1):125-140, 190, 201, 1990.
Anatoly A. Zhigljavsky.
Theory of global random search, volume 65 of Mathematics
and its Applications (Soviet Series).
Kluwer Academic Publishers Group, Dordrecht, 1991.
Translated and revised from the 1985 Russian original by the author,
With a foreword by Sergei M. Ermakov.
Anatoly A. Zhigljavsky.
Semiparametric statistical inference in global random search.
Acta Appl. Math., 33(1):69-88, 1993.
Stochastic optimization.
A. Zhilinskas.
On the multimodal minimization algorithm constructed axiomatically.
Methods Oper. Res. 40, 197-200, 1981.
A. Zhilinskas and A. Katkauskajte.
On statistical models of global optimization in the presence of
noise.
Teor. Optim. Reshenij 12, 43-53, 1987.
A. Zhilinskas and È. Senkene.
On the convergence of one-dimensional one-step algorithms of
multiextremal optimization in the presence of noise.
Litovsk. Mat. Sb., 21(1):41-46, 1981.
A. Zhilinskas and Eh. Senkene.
Convergence of one-dimensional one-stage algorithms for
multiextremal optimization in the presence of noise.
Lith. Math. J. 21, 12-15, 1981.
A.G. Zhilinskas.
Statistical models of multi-extremal functions and the construction
of global optimization algorithms.
In Optimization: models, methods, solutions (Russian) (Irkutsk,
1989), pages 81-90. "Nauka" Sibirsk. Otdel., Novosibirsk, 1992.
Antanas Zhilinskas.
On models of complicated functions under uncertainty.
In Information theory, statistical decision functions, random
processes, Trans. 9th Prague Conf., Prague 1982, Vol. A, 113-118, 1983.
Antanas Zhilinskas and Aldona Katkauskait\.e.
Statistical models for global optimization in the presence of noise.
Teor. Optimal. Reshenii, 12:43-53, 1987.
L.S. Zhitetskii.
A recursive algorithm for adaptive control of a static object.
Kibernet. i Vychisl. Tekhn., 49:67-74, 99, 1980.
Wei Jun Zhong, Nan Rong Xu, and Sen Fa Chen.
A stochastic global optimization algorithm for multi-person and
two-level decision problems.
J. Systems Engrg., 7(2):20-28, 1992.
Chang Yin Zhou and Guo Ping He.
Approximate Lagrange-Newton algorithms for a class of stochastic
programming problems.
J. Shandong Univ. Sci. Technol. Nat. Sci., 24(2):80-83, 2005.
Chang Yin Zhou, Guo Ping He, and Shu Shan Li.
A stochastic-search-based global convergence algorithm for
constrained optimization problems.
Appl. Math. J. Chinese Univ. Ser. A, 20(2):197-206, 2005.
Changyin Zhou and Guoping He.
An inexact Lagrange-Newton method for stochastic quadratic
programs with recourse.
Appl. Math. J. Chinese Univ. Ser. B, 19(2):229-238, 2004.
Dao Li Zhu and Zhong Xue Lei.
Subconcave functions and a study on convexity with respect to the
normal distribution in stochastic chance-constrained programming.
J. Shanghai Jiaotong Univ., 24(3):77-81, 1990.
Guidong Zhu, Jonathan F. Bard, and Gang Yu.
A two-stage stochastic programming approach for project planning with
uncertain activity durations.
J. Sched., 10(3):167-180, 2007.
Xin Shu Zhu.
Sequential estimation in stochastic programming.
Nanjing Daxue Xuebao Shuxue Bannian Kan, 14(1):133-137, 1997.
Yaochen Zhu.
On the convergence of sequential number-theoretic method for
optimization.
Acta Math. Appl. Sinica (English Ser.), 17(4):532-538, 2001.
V.I. Zhukovski and D.T. Dochev.
A p1-optimal solution of a multicriterial problem with
uncertainty.
Godishnik Vissh. Uchebn. Zaved. Prilozhna Mat., 23(1):9-18
(1988), 1987.
V.I. Zhukovski and D.T. Dochev.
A p2-optimal solution of a multicriterial problem with
uncertainty.
Godishnik Vissh. Uchebn. Zaved. Prilozhna Mat., 23(2):27-40
(1988), 1987.
S.V. Zhulenev.
Ein Normalmodell in Problemen der stochastischen Programmierung.
Akad. Nauk Arm. SSR, Dokl. 50, 268-272, 1970.
S.V. Zhulenev.
Duality in stochastic programming problems with a random plan.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1971, No.4, 19-26, 1971.
S.V. Zhulenev.
On a method for seeking a global extremum.
Engrg. Cybernetics, 12:32-38, 1974.
Corneliu Zidaroiu.
Undiscounted random decision systems with complete connections.
Revue Roumaine Math. pur. appl. 21, 487-494, 1976.
Ryszard Zielinski.
A Monte Carlo estimation of the maximum of a function.
Algorytmy 7, 5-7, 1970.
Ryszard Zielinski.
Global stochastic approximation: A review of results and some open
problems.
In Numerical techniques for stochastic systems, Conf.
Gargnano/Italy 1979, 379-386, 1980.
Ryszard Zieli\'nski and Peter Neumann.
Stochastische Verfahren zur Suche nach dem Minimum einer
Funktion, volume 16 of Mathematical Research.
Akademie-Verlag, Berlin, 1983.
Ryszard Zieli\'nski and Peter Neumann.
Stochastyczne metody poszukiwania minimum funkcji.
Wydawnictwa Naukowo-Techniczne (WNT), Warsaw, 1986.
Translated from the German by Zieli\'nski.
William T. Ziemba.
Duality relations, certainty equivalents and bounds for convex
stochastic programs with simple recourse.
Cahiers Centre Études Recherche Opér., 13:85-97, 1971.
William T. Ziemba.
Transforming stochastic dynamic programming problems into nonlinear
programs.
Management Sci., Theory 17, 450-462, 1971.
William T. Ziemba.
Stochastic programs with simple recourse.
In Mathematical programming in theory and practice (Proc. NATO
Adv. Study Inst., Figueira da Foz, 1972), pages 213-273, Amsterdam, 1974.
North-Holland.
W.T. Ziemba.
Stochastic programs with simple recourse.
In P.L. Hammer and G. Zoutendijk, editors, Mathematical
Programming in Theory and Practice, pages 213-273. North-Holland Publishing
Company, Amsterdam, 1974.
W.T. Ziemba and J.M. Mulvey, editors.
World Wide Asset and Liability Management.
Cambridge Univ. Press, 1998.
W.T. Ziemba and R.G. Vickson, editors.
Stochastic optimization models in finance.
Academic Press, New York, 1975.
Mynt Zijlstra.
Optimization of repair limit replacement policies for one-unit
systems.
RAIRO Rech. Opér., 15(4):351-361, 1981.
Itzhak Zilcha.
Weakly maximal optimal stationary program under uncertainty.
Internat. econom. Review 16, 796-799, 1975.
Itzhak Zilcha.
Characterization by prices of optimal programs under uncertainty.
J. math. Economics 3, 173-183, 1976.
A. Zilinskas.
Optimization of one-dimensional multimodal functions.
J. R. Stat. Soc., Ser. C 27, 367-375, 1978.
A. Zilinskas.
The use of statistical models for construction of multimodal
optimization algorithms.
In Information theory, Proc. 3rd Czech.-Sov.-Hung. Semin.,
Liblice 1980, 219- 224, 1980.
A. Zilinskas.
Two algorithms for one-dimensional multimodal minimization.
Math. Operationsforsch. Statist. Ser. Optim., 12(1):53-63,
1981.
A. Zilinskas.
Axiomatic approach to statistical models and their use in multimodal
optimization theory.
Math. Program. 22, 104-116, 1982.
A. Zilinskas.
Multimodal minimization algorithm constructed axiomatically: Proof
of convergence and testing results.
Wiss. Z. Hochsch. Archit. Bauwes. Weimar 28, 226-228, 1982.
A. Zilinskas.
On justification of use of stochastic functions for multimodal
optimization models.
In Stochastics and optimization, Sel. Pap. ISSO Meet.,
Gorguano/Italy 1982, Ann. Oper. Res. 1, 129-134, 1984.
A. Zilinskas.
A review of statistical models for global optimization.
J. Global Optim., 2(2):145-153, 1992.
A. Zilinskas and J. Zilinskas.
Global optimization based on a statistical model and simplicial
partitioning.
Comput. Math. Appl., 44(7):957-967, 2002.
Global optimization, control, and games, IV.
A.G. Zilinskas and I.B. Mockus.
A certain Bayesian method of search for the minimum.
Avtomat. i Vycisl. Tehn. (Riga), 4:42-44, 1972.
A.G. Zilinskas, I.B. Mockus, and L.L. Timofeev.
A Bayesian method of extremal search with restricted memory.
Avtomat. i Vycisl. Tehn. (Riga), 6:37-42, 1972.
Antanas Zilinskas.
Mimun. Optimization of one-dimensional multimodal functions in the
presence of noise.
Apl. Mat. 25, 234-240, 1980.
Antanas Zilinskas.
Statistical models of multimodal functions and construction of
algorithms for global optimization.
Informatica, 1(1):141-155, 191, 202, 1990.
H.-J. Zimmermann.
Neuere Entwicklung auf dem Gebiet der stochastischen Programmierung
(Uebersichtsvortrag).
In Proc. Oper. Res. 3, DGOR Ann. Meet. Karlsruhe 1973, 43-60,
1974.
H.-J. Zimmermann.
Fuzzy set theory and mathematical programming.
In Fuzzy sets theory and applications, Proc. NATO Adv. Study
Inst., Louvain- la-Neuve/Belg. 1985, NATO ASI Ser., Ser. C 177, 99-114,
1986.
H.-J. Zimmermann and M.A. Pollatschek.
The domain of the "resource-vector" as an aid to decision making in
stochastic 0/1 programming.
In Operations Res.-Verf. 14, 390-398, 1972.
H.-J. Zimmermann and M.A. Pollatschek.
On stochastic integer programming.
Z. Operations Res. Ser. A-B, 19:A37-A48, 1975.
H.-J. Zimmermann and M.A. Pollatschek.
The probability distribution function of the optimum of a 0-1\
linear program with randomly distributed coefficients of the objective
function and the right-hand side.
Operations Res., 23(1):137-149, 1975.
Hans-Juergen Zimmermann.
Methoden und Modelle des Operations Research. Fuer Ingenieure,
Oekonomen und Informatiker. (Methods and models of operations research. For
engineers, economists and computer scientists).
Rechnerorientierte Ingenieurmathematik. Braunschweig/Wiesbaden:
Friedr. Vieweg & Sohn. XIV, 364 S., 1987.
Mincho P. Zlatev.
Optimization analysis. A principled synthesized generalization.
Problemi Tekhn. Kibernet. Robot., 20:12-27, 1984.
M.A. Zohdy, M.S. Fadali, and J. Liu.
Robust design of discrete-time systems via optimization.
Internat. J. Systems Sci., 24(4):797-803, 1993.
S.V. Zulenev.
A normal model in stochastic programming problems. A special case.
In Mathematical methods for the solution of economic problems
(Suppl. to Èkonom. i Mat. Metody), No. 3 (Russian), pages 59-68. Izdat.
"Nauka", Moscow, 1972.
S.V. Zulenev.
Ueber eine Methode zum Suchen eines globalen Extremums.
Izv. Akad. Nauk SSSR, tehn. Kibernet. 1974, Nr. 2, 46-52, 1974.
File translated from
TEX
by
TTH,
version 3.77 on 08 Oct 2007.