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Aggregation and discretization in multistage stochastic programming

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abstract 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.
   
type journal paper
   
keywords Stochastic programming, Approximation, Bounds, Aggregation, Discretization
   
language English
kind of paper journal article
date of appearance 1-5-2008
journal Mathematical Programming, Series A
publisher Springer (Berlin / Heidelberg)
ISSN 0025-5610
ISSN (online) 1436-4646
number of issue 113 (1)
page(s) 61-94
review double-blind review
   
citation Kuhn, D. (2008). Aggregation and discretization in multistage stochastic programming. Mathematical Programming, Series A(113 (1)), 61-94.