On the Convergence of Decomposition Methods for Multistage Stochastic Convex Programs

Abstract : We prove the almost-sure convergence of a class of sampling-based nested decomposition algorithms for multistage stochastic convex programs in which the stage costs are general convex functions of the decisions , and uncertainty is modelled by a scenario tree. As special cases, our results imply the almost-sure convergence of SDDP, CUPPS and DOASA when applied to problems with general convex cost functions.
Document type :
Journal articles
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download

https://hal-enpc.archives-ouvertes.fr/hal-01208295
Contributor : Vincent Leclère <>
Submitted on : Friday, October 2, 2015 - 12:34:34 PM
Last modification on : Thursday, July 4, 2019 - 7:03:36 AM
Long-term archiving on : Sunday, January 3, 2016 - 10:48:29 AM

File

ConvergenceOfDecompositionMeth...
Files produced by the author(s)

Identifiers

Citation

Pierre Girardeau, Vincent Leclere, A. B. Philpott. On the Convergence of Decomposition Methods for Multistage Stochastic Convex Programs. Mathematics of Operations Research, INFORMS, 2015, 40 (1), ⟨10.1287/moor.2014.0664⟩. ⟨hal-01208295⟩

Share

Metrics

Record views

188

Files downloads

171