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Adaptive discretization of convex multistage stochastic programs

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  • Stefan Vigerske
  • Ivo Nowak

Abstract

We propose a new scenario tree reduction algorithm for multistage stochastic programs, which integrates the reduction of a scenario tree into the solution process of the stochastic program. This allows to construct a scenario tree that is highly adapted on the optimization problem. The algorithm starts with a rough approximation of the original tree and locally refines this approximation as long as necessary. Promising numerical results for scenario tree reductions in the settings of portfolio management and power management with uncertain load are presented. Copyright Springer-Verlag 2007

Suggested Citation

  • Stefan Vigerske & Ivo Nowak, 2007. "Adaptive discretization of convex multistage stochastic programs," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 65(2), pages 361-383, April.
    • Handle: RePEc:spr:mathme:v:65:y:2007:i:2:p:361-383
    DOI: 10.1007/s00186-006-0124-y
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    References listed on IDEAS

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    1. 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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