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A splitting method for stochastic programs

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  • Teemu Pennanen
  • Markku Kallio

Abstract

This paper derives a new splitting-based decomposition algorithm for convex stochastic programs. It combines certain attractive features of the progressive hedging algorithm of Rockafellar and Wets, the dynamic splitting algorithm of Salinger and Rockafellar and an algorithm of Korf. We give two derivations of our algorithm. The first one is very simple, and the second one yields a preconditioner that resulted in a considerable speed-up in our numerical tests. Copyright Springer Science + Business Media, Inc. 2006

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  • Teemu Pennanen & Markku Kallio, 2006. "A splitting method for stochastic programs," Annals of Operations Research, Springer, vol. 142(1), pages 259-268, February.
    • Handle: RePEc:spr:annopr:v:142:y:2006:i:1:p:259-268:10.1007/s10479-006-6171-1
    DOI: 10.1007/s10479-006-6171-1
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    References listed on IDEAS

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    1. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    2. Blomvall, Jorgen & Lindberg, Per Olov, 2002. "A Riccati-based primal interior point solver for multistage stochastic programming," European Journal of Operational Research, Elsevier, vol. 143(2), pages 452-461, December.
    3. Jonathan Eckstein & Michael C. Ferris, 1998. "Operator-Splitting Methods for Monotone Affine Variational Inequalities, with a Parallel Application to Optimal Control," INFORMS Journal on Computing, INFORMS, vol. 10(2), pages 218-235, May.
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