On the Convergence of Decomposition Methods for Multistage Stochastic Convex Programs

Pierre Girardeau, Vincent Leclère and A. B. Philpott

february, 2015

Publication type:

Paper in peer-reviewed journals

Journal:

Mathematics of Operations Research, vol. 40 (1)

Publisher:

INFORMS

External link:

http://www.optimization-online.org/DB_HTML/2012/04/3445.html

DOI:

10.1287/moor.2014.0664

HAL:

hal-01208295

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.

BibTeX:

@article{Gir-Lec-Phi-2015,
    author={Pierre Girardeau and Vincent Leclère and A. B. Philpott },
    title={On the Convergence of Decomposition Methods for Multistage 
           Stochastic Convex Programs },
    doi={10.1287/moor.2014.0664 },
    journal={Mathematics of Operations Research },
    year={2015 },
    month={2},
    volume={40 (1) },
}