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Scenario generation for stochastic optimization problems via the sparse grid method

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  • Michael Chen
  • Sanjay Mehrotra
  • Dávid Papp

Abstract

We study the use of sparse grids in the scenario generation (or discretization) problem in stochastic programming problems where the uncertainty is modeled using a continuous multivariate distribution. We show that, under a regularity assumption on the random function involved, the sequence of optimal objective function values of the sparse grid approximations converges to the true optimal objective function values as the number of scenarios increases. The rate of convergence is also established. We treat separately the special case when the underlying distribution is an affine transform of a product of univariate distributions, and show how the sparse grid method can be adapted to the distribution by the use of quadrature formulas tailored to the distribution. We numerically compare the performance of the sparse grid method using different quadrature rules with classic quasi-Monte Carlo (QMC) methods, optimal rank-one lattice rules, and Monte Carlo (MC) scenario generation, using a series of utility maximization problems with up to 160 random variables. The results show that the sparse grid method is very efficient, especially if the integrand is sufficiently smooth. In such problems the sparse grid scenario generation method is found to need several orders of magnitude fewer scenarios than MC and QMC scenario generation to achieve the same accuracy. It is indicated that the method scales well with the dimension of the distribution—especially when the underlying distribution is an affine transform of a product of univariate distributions, in which case the method appears scalable to thousands of random variables. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Michael Chen & Sanjay Mehrotra & Dávid Papp, 2015. "Scenario generation for stochastic optimization problems via the sparse grid method," Computational Optimization and Applications, Springer, vol. 62(3), pages 669-692, December.
    • Handle: RePEc:spr:coopap:v:62:y:2015:i:3:p:669-692
    DOI: 10.1007/s10589-015-9751-7
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    References listed on IDEAS

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    1. Heiss, Florian & Winschel, Viktor, 2008. "Likelihood approximation by numerical integration on sparse grids," Journal of Econometrics, Elsevier, vol. 144(1), pages 62-80, May.
    2. M.A.H. Dempster & R.T. Thompson, 1999. "EVPI‐based importance sampling solution proceduresfor multistage stochastic linear programmeson parallel MIMD architectures," Annals of Operations Research, Springer, vol. 90(0), pages 161-184, January.
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    4. Stein W. Wallace & Stein-Erik Fleten, 2002. "Stochastic programming in energy," GE, Growth, Math methods 0201001, University Library of Munich, Germany, revised 13 Nov 2003.
    5. Jitka Dupačová & Giorgio Consigli & Stein Wallace, 2000. "Scenarios for Multistage Stochastic Programs," Annals of Operations Research, Springer, vol. 100(1), pages 25-53, December.
    6. Michael S. Casey & Suvrajeet Sen, 2005. "The Scenario Generation Algorithm for Multistage Stochastic Linear Programming," Mathematics of Operations Research, INFORMS, vol. 30(3), pages 615-631, August.
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    Cited by:

    1. Julien Keutchayan & Michel Gendreau & Antoine Saucier, 2017. "Quality evaluation of scenario-tree generation methods for solving stochastic programming problems," Computational Management Science, Springer, vol. 14(3), pages 333-365, July.
    2. Zhang, Dongqing & Wallace, Stein W. & Guo, Zhaoxia & Dong, Yucheng & Kaut, Michal, 2021. "On scenario construction for stochastic shortest path problems in real road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    3. Julien Keutchayan & Janosch Ortmann & Walter Rei, 2023. "Problem-driven scenario clustering in stochastic optimization," Computational Management Science, Springer, vol. 20(1), pages 1-33, December.

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