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The empirical behavior of sampling methods for stochastic programming


  • Jeff Linderoth


  • Alexander Shapiro


  • Stephen Wright



We investigate the quality of solutions obtained from sample-average approximations to two-stage stochastic linear programs with recourse. We use a recently developed software tool executing on a computational grid to solve many large instances of these problems, allowing us to obtain high-quality solutions and to verify optimality and near-optimality of the computed solutions in various ways. Copyright Springer Science + Business Media, Inc. 2006

Suggested Citation

  • Jeff Linderoth & Alexander Shapiro & Stephen Wright, 2006. "The empirical behavior of sampling methods for stochastic programming," Annals of Operations Research, Springer, vol. 142(1), pages 215-241, February.
  • Handle: RePEc:spr:annopr:v:142:y:2006:i:1:p:215-241:10.1007/s10479-006-6169-8
    DOI: 10.1007/s10479-006-6169-8

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    References listed on IDEAS

    1. T. Glenn Bailey & Paul A. Jensen & David P. Morton, 1999. "Response surface analysis of two‐stage stochastic linear programming with recourse," Naval Research Logistics (NRL), John Wiley & Sons, vol. 46(7), pages 753-776, October.
    2. Athanassios N. Avramidis & James R. Wilson, 1996. "Integrated Variance Reduction Strategies for Simulation," Operations Research, INFORMS, vol. 44(2), pages 327-346, April.
    3. Julia L. Higle & Suvrajeet Sen, 1991. "Stochastic Decomposition: An Algorithm for Two-Stage Linear Programs with Recourse," Mathematics of Operations Research, INFORMS, vol. 16(3), pages 650-669, August.
    4. Julia L. Higle, 1998. "Variance Reduction and Objective Function Evaluation in Stochastic Linear Programs," INFORMS Journal on Computing, INFORMS, vol. 10(2), pages 236-247, May.
    5. John M. Mulvey & Andrzej Ruszczyński, 1995. "A New Scenario Decomposition Method for Large-Scale Stochastic Optimization," Operations Research, INFORMS, vol. 43(3), pages 477-490, June.
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