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On Complexity of Stochastic Programming Problems

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Continuous Optimization

Part of the book series: Applied Optimization ((APOP,volume 99))

Summary

The main focus of this paper is in a discussion of complexity of stochastic programming problems. We argue that two-stage (linear) stochastic programming problems with recourse can be solved with a reasonable accuracy by using Monte Carlo sampling techniques, while multistage stochastic programs, in general, are intractable. We also discuss complexity of chance constrained problems and multistage stochastic programs with linear decision rules.

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Author information

Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, Georgia, 30332-0205, USA

    Alexander Shapiro

  2. Technion — Israel Institute of Technology, Haifa, 32000, Israel

    Arkadi Nemirovski

Authors
  1. Alexander Shapiro
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  2. Arkadi Nemirovski
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Editor information

Editors and Affiliations

  1. University of New South Wales, Sydney, Australia

    Vaithilingam Jeyakumar

  2. University of Ballarat, Ballarat, Australia

    Alexander Rubinov

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Shapiro, A., Nemirovski, A. (2005). On Complexity of Stochastic Programming Problems. In: Jeyakumar, V., Rubinov, A. (eds) Continuous Optimization. Applied Optimization, vol 99. Springer, Boston, MA. https://doi.org/10.1007/0-387-26771-9_4

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