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research article
Aggregation and discretization in multistage stochastic programming
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
research article
Authors
Publication date
2008
Published in
Volume
113
Issue
1
Start page
61
End page
94
Peer reviewed
NON-REVIEWED
EPFL units
Available on Infoscience
January 21, 2014
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