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On the convergence of decomposition methods for multi-stage stochastic convex programs
P. Girardeau (pierre.girardeau 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. Keywords: Stochastic Programming, Dynamic Programming, Stochastic Dual Dynamic Programming algorithm, Monte-Carlo sampling, Benders decomposition Category 1: Stochastic Programming Category 2: Other Topics (Dynamic Programming ) Category 3: Convex and Nonsmooth Optimization (Convex Optimization ) Citation: Download: [PDF] Entry Submitted: 04/24/2012 Modify/Update this entry | ||
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