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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.
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Submitted on : Friday, October 2, 2015 - 12:34:34 PM
Last modification on : Friday, December 3, 2021 - 11:34:09 AM
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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⟩

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