| edoc-Server der Humboldt-Universität zu Berlin |
| Author(s): | Daniel Kuhn, University of St. Gallen | Title: | Aggregation and Discretization in Multistage Stochastic Programming |
| Date of Acceptance: | 28.12.2005 |
| Submission Date: | 30.06.2005 |
| Series Title: |
Stochastic Programming E-Print Series (SPEPS) |
| Editors: | Julie L. Higle; Werner Römisch; Surrajeet Sen |
| Complete Preprint: | pdf (urn:nbn:de:kobv:11-10059940) |
| Keywords (eng): | aggregation, stochastic programming, approximation, discretization, bounds |
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| Abstract (eng): | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Multistage stochastic programs have applications in many areas and support policy makers in finding rational decisions that hedge against unforeseen neg- ative 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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