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Decomposition methods in stochastic programming

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Abstract

Stochastic programming problems have very large dimension and characteristic structures which are tractable by decomposition. We review basic ideas of cutting plane methods, augmented Lagrangian and splitting methods, and stochastic decomposition methods for convex polyhedral multi-stage stochastic programming problems.

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Author notes

  1. Andrzej Ruszczyński

    Present address: Department of Electronics and Computer Science, Warsaw University of Technology, Poland

Authors and Affiliations

  1. Department of Industrial Engineering, University of Wisconsin-Madison, 1513 University Avenue, 53706, Madison, WI, USA

    Andrzej Ruszczyński

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  1. Andrzej Ruszczyński
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Ruszczyński, A. Decomposition methods in stochastic programming. Mathematical Programming 79, 333–353 (1997). https://doi.org/10.1007/BF02614323

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  • DOI: https://doi.org/10.1007/BF02614323

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