Scenario Reduction with Respect to Discrepancy Distances

Christian Küchler

Chapter 4 in Stability, Approximation, and Decomposition in Two- and Multistage Stochastic Programming, 2009, pp 97-151 from Springer

Abstract: Zusammenfassung As we have discussed in the previous chapters, many stochastic optimization problems do not allow for an analytic solution, and, hence, one has to resort to numerical approaches. However, numerical approaches usually require the underlying probability measures to have only a finite support. While such finite measures can be obtained, e.g., by sampling or from historical data, the number of atoms (or, scenarios) has to be in general sufficiently small to maintain the numerical tractability. Approximating a (finite) probability measure by a measure with a smaller number of atoms is denoted as scenario reduction in the literature.

Keywords: Stochastic Program; Forward Selection; Discrepancy Distance; Probability Weight; Linear Optimization Problem (search for similar items in EconPapers)
Date: 2009
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-8348-9399-4_4

Ordering information: This item can be ordered from
http://www.springer.com/9783834893994

DOI: 10.1007/978-3-8348-9399-4_4

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().