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MP
2008

Aggregation and discretization in multistage stochastic programming

13 years 3 months ago
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. Key words. stochastic programming, approximation, bounds, aggregation, discretization
Daniel Kuhn
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2008
Where MP
Authors Daniel Kuhn
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