Additions to the Stochastic Programming Bibliography

Additions to the Stochastic Programming Bibliography

Maarten H. van der Vlerk
Department of Operations
University of Groningen
PO Box 800, NL-9700 AV Groningen, The Netherlands
E-mail: m.h.van.der.vlerk@rug.nl

October 8, 2007
Additions to the Stochastic Programming Bibliography since the previous update (May 2, 2003).
Please send further additions (preferably in BibTeX format) or comments to the e-mail address mentioned above.

References

  1. J. Abaffy and E. Allevi. A modified L-shaped method. J. Optim. Theory Appl., 123(2):255-270, 2004.
  2. Moncef Abbas and Fatima Bellahcene. Cutting plane method for multiple objective stochastic integer linear programming. European J. Oper. Res., 168(3):967-984, 2006.
  3. 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.
  4. Shabbir Ahmed. Mean-risk objectives in stochastic programming. Stochastic Programming E-Print Series, http://www.speps.org, 2004.
  5. Shabbir Ahmed. Convexity and decomposition of mean-risk stochastic programs. Math. Program., 106(3, Ser. A):433-446, 2006.
  6. Shabbir Ahmed. Smooth minimization of two-stage stochastic linear programs. Optimization Online, http://www.optimization-online.org, 2006.
  7. Shabbir Ahmed, Ulas Cakmak, and Alexander Shapiro. Coherent risk measures in inventory problems. Stochastic Programming E-Print Series, http://www.speps.org, 2006.
  8. 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.
  9. Maria Albareda-Sambola and Elena Fernández. The stochastic generalised assignment problem with Bernoulli demands. Top, 8(2):165-190, 2000.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. Mahmoud H. Alrefaei and Mohammad Almomani. Subset selection of best simulated systems. J. Franklin Inst., 344(5):495-506, 2007.
  18. Mahmoud H. Alrefaei and Sigrún Andradóttir. Discrete stochastic optimization using variants of the stochastic ruler method. Naval Res. Logist., 52(4):344-360, 2005.
  19. 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.
  20. 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.
  21. K. A. Ariyawansa and Yuntao Zhu. Stochastic semidefinite programming: a new paradigm for stochastic optimization. 4OR, 4(3):239-253, 2006.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. Matthew D. Bailey, Steven M. Shechter, and Andrew J. Schaefer. SPAR: stochastic programming with adversarial recourse. Oper. Res. Lett., 34(3):307-315, 2006.
  27. 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.
  28. 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.
  29. Bill Baritompa and Eligius M. T. Hendrix. On the investigation of stochastic global optimization algorithms. J. Global Optim., 31(4):567-578, 2005.
  30. 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.
  31. Diana Barro and Elio Canestrelli. A decomposition approach in multistage stochastic programming. Rend. Studi Econ. Quant., pages 73-88, 2005.
  32. 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.
  33. Kengy Barty, Jean-Sebastien Roy, and Cyrille Strugarek. A perturbed gradient algorithm in hilbert spaces. Optimization Online, http://www.optimization-online.org, 2005.
  34. 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.
  35. 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.
  36. 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.
  37. Güzin Bayraksan and David P. Morton. Assessing solution quality in stochastic programs. Stochastic Programming E-Print Series, http://www.speps.org, 2005.
  38. Güzin Bayraksan and David P. Morton. Assessing solution quality in stochastic programs. Math. Program., 108(2-3, Ser. B):495-514, 2006.
  39. 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.
  40. 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.
  41. H. Beigy and M. R. Meybodi. Stochastic optimization using continuous action-set learning automata. Sci. Iran., 12(1):14-25, 2005.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. Marida Bertocchi, Vittorio Moriggia, and Jitka Dupacová. Horizon and stages in applications of stochastic programming in finance. Ann. Oper. Res., 142:63-78, 2006.
  49. Dimitris Bertsimas and Melvyn Sim. Tractable approximations to robust conic optimization problems. Math. Program., 107(1-2, Ser. B):5-36, 2006.
  50. 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.
  51. 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.
  52. M. P. Biswal, N. P. Sahoo, and Duan Li. Probabilistic linearly constrained programming problems with lognormal random variables. Opsearch, 42(1):70-76, 2005.
  53. 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.
  54. 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.
  55. Jozsef Bukszar, René Henrion, Mihaly Hujter, and Tamas Szantai. Polyhedral inclusion-exclusion. Stochastic Programming E-Print Series, http://www.speps.org, 2004.
  56. 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.
  57. 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.
  58. 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.
  59. G. C. Calafiore and L. El Ghaoui. On distributionally robust chance-constrained linear programs. J. Optim. Theory Appl., 130(1):1-22, 2006.
  60. 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.
  61. J. M. Calvin and A. Zilinskas. One-dimensional global optimization for observations with noise. Comput. Math. Appl., 50(1-2):157-169, 2005.
  62. 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.
  63. Andrew Caplin and John Leahy. The recursive approach to time inconsistency. J. Econom. Theory, 131(1):134-156, 2006.
  64. Michael S. Casey and Suvrajeet Sen. The scenario generation algorithm for multistage stochastic linear programming. Math. Oper. Res., 30(3):615-631, 2005.
  65. 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.
  66. 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.
  67. Hyeong Soo Chang. Multi-policy improvement in stochastic optimization with forward recursive function criteria. J. Math. Anal. Appl., 305(1):130-139, 2005.
  68. 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.
  69. 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.
  70. 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.
  71. V. Charles and D. Dutta. Linear stochastic fractional programming with sum-of-probabilistic-fractional objective. Optimization Online, http://www.optimization-online.org, 2005.
  72. V. Charles and D. Dutta. Non-linear stochastic fractional programming models of financial derivatives. Optimization Online, http://www.optimization-online.org, 2005.
  73. 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.
  74. 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.
  75. Shih-Pin Chen. An alternating variable method with varying replications for simulation response optimization. Comput. Math. Appl., 48(5-6):769-778, 2004.
  76. 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).
  77. 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.
  78. 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.
  79. 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.
  80. Anukal Chiralaksanakul and David P. Morton. Assessing policy quality in multi-stage stochastic programming. Stochastic Programming E-Print Series, http://www.speps.org, 2004.
  81. 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).
  82. Gyeong-Mi Cho. Log-barrier method for two-stage quadratic stochastic programming. Appl. Math. Comput., 164(1):45-69, 2005.
  83. 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.
  84. 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.
  85. Andre Costa. Ants, stochastic optimisation and reinforcement learning. Austral. Math. Soc. Gaz., 32(2):116-123, 2005.
  86. 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.
  87. 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.
  88. 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.
  89. 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.
  90. 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.
  91. 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.
  92. István Deák. Two-stage stochastic problems with correlated normal variables: computational experiences. Ann. Oper. Res., 142:79-97, 2006.
  93. 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.
  94. 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.
  95. 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.
  96. D. Dentcheva and A. Ruszczy\'nski. Inverse stochastic dominance constraints and quantile utility theory. C. R. Acad. Bulgare Sci., 58(1):13-18, 2005.
  97. 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.
  98. 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.
  99. Darinka Dentcheva, Bogumila Lai, and Andrzej Ruszczy\'nski. Dual methods for probabilistic optimization problems. Math. Methods Oper. Res., 60(2):331-346, 2004.
  100. 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).
  101. Darinka Dentcheva and Werner Römisch. Duality gaps in nonconvex stochastic optimization. Math. Program., 101(3, Ser. A):515-535, 2004.
  102. 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.
  103. Darinka Dentcheva and Andrzej Ruszczy\'nski. Optimization with stochastic dominance constraints. Stochastic Programming E-Print Series, http://www.speps.org, 2003.
  104. Darinka Dentcheva and Andrzej Ruszczy\'nski. Portfolio optimization with stochastic dominance constraints. Stochastic Programming E-Print Series, http://www.speps.org, 2003.
  105. Darinka Dentcheva and Andrzej Ruszczy\'nski. Convexification of stochastic ordering. Stochastic Programming E-Print Series, http://www.speps.org, 2004.
  106. 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.
  107. Darinka Dentcheva and Andrzej Ruszczy\'nski. Semi-infinite probabilistic optimization: first-order stochastic dominance constraints. Optimization, 53(5-6):583-601, 2004.
  108. 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.
  109. 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.
  110. 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.
  111. 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.
  112. 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.
  113. 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.
  114. 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.
  115. 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.
  116. 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.
  117. Jitka Dupacová. Contamination for multistage stochastic programs. Stochastic Programming E-Print Series, http://www.speps.org, 2006.
  118. 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.
  119. Jitka Dupacová and Jan Polivka. Stress testing for var an cvar. Stochastic Programming E-Print Series, http://www.speps.org, 2005.
  120. Jitka Dupacová and Pavel Popela. Melt control. Stochastic Programming E-Print Series, http://www.speps.org, 2004.
  121. 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.
  122. Shane Dye. Subtree decomposition for multistage stochastic programs. Stochastic Programming E-Print Series, http://www.speps.org, 2003.
  123. 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.
  124. Martin Dyer and Leen Stougie. Computational complexity of stochastic programming problems. Math. Program., 106(3, Ser. A):423-432, 2006.
  125. 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).
  126. 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.
  127. Andreas Eichhorn and Werner Römisch. Polyhedral risk measures in stochastic programming. Stochastic Programming E-Print Series, http://www.speps.org, 2004.
  128. Andreas Eichhorn and Werner Römisch. Polyhedral risk measures in stochastic programming. SIAM J. Optim., 16(1):69-95 (electronic), 2005.
  129. Andreas Eichhorn and Werner Römisch. Stochastic integer programming. Stochastic Programming E-Print Series, http://www.speps.org, 2005.
  130. Andreas Eichhorn and Werner Römisch. Stability of multistage stochastic programs incorporating polyhedral risk measures. Stochastic Programming E-Print Series, http://www.speps.org, 2006.
  131. Andreas Eichhorn and Werner Römisch. Stochastic integer programming: limit theorems and confidence intervals. Math. Oper. Res., 32(1):118-135, 2007.
  132. 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.
  133. 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.
  134. 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.
  135. 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.
  136. E. Erdogan and G. Iyengar. Ambiguous chance constrained problems and robust optimization. Stochastic Programming E-Print Series, http://www.speps.org, 2005.
  137. E. Erdogan and G. Iyengar. Ambiguous chance constrained problems and robust optimization. Math. Program., 107(1-2, Ser. B):37-61, 2006.
  138. E. Erdogan and G. Iyengar. On two-stage convex chance constrained problems. Stochastic Programming E-Print Series, http://www.speps.org, 2006.
  139. 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.
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