Mathematical Programming

, Volume 113, Issue 1, pp 61–94 | Cite as

Aggregation and discretization in multistage stochastic programming

  • Daniel Kuhn


Multistage stochastic programs have applications in many areas and support policy makers in finding rational decisions that hedge against unforeseen negative events. In order to ensure computational tractability, continuous-state stochastic programs are usually discretized; and frequently, the curse of dimensionality dictates that decision stages must be aggregated. In this article we construct two discrete, stage-aggregated stochastic programs which provide upper and lower bounds on the optimal value of the original problem. The approximate problems involve finitely many decisions and constraints, thus principally allowing for numerical solution.


Stochastic programming Approximation Bounds Aggregation Discretization 

Mathematics Subject Classification (2000)

90C15 90C25 49M29 


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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Institute for Operations Research and Computational Finance (ior/cf-HSG)University of St. GallenSt. GallenSwitzerland

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