Dynamic generation of scenario trees

Georg Pflug and Alois Pichler ()

Computational Optimization and Applications, 2015, vol. 62, issue 3, 668 pages

Abstract: This paper presents new algorithms for the dynamic generation of scenario trees for multistage stochastic optimization. The different methods described are based on random vectors, which are drawn from conditional distributions given the past and on sample trajectories. The structure of the tree is not determined beforehand, but dynamically adapted to meet a distance criterion, which measures the quality of the approximation. The criterion is built on transportation theory, which is extended to stochastic processes. Copyright Springer Science+Business Media New York 2015

Keywords: Decision trees; Stochastic optimization; Optimal transportation; 90C15; 60B05; 62P05 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
http://hdl.handle.net/10.1007/s10589-015-9758-0 (text/html)
Access to full text is restricted to subscribers.

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:coopap:v:62:y:2015:i:3:p:641-668

Ordering information: This journal article can be ordered from
http://www.springer.com/math/journal/10589

DOI: 10.1007/s10589-015-9758-0

Access Statistics for this article

Computational Optimization and Applications is currently edited by William W. Hager

More articles in Computational Optimization and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().