Aggregation and discretization in multistage stochastic programming
Journal
Mathematical Programming, Series A
ISSN
0025-5610
ISSN-Digital
1436-4646
Type
journal article
Date Issued
2008-05-01
Author(s)
Kuhn, Daniel
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.
Language
English
Keywords
Stochastic programming
Approximation
Bounds
Aggregation
Discretization
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Springer
Publisher place
Berlin
Volume
113
Number
1
Start page
61
End page
94
Pages
34
Subject(s)
Division(s)
Eprints ID
60637