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DASC: a Decomposition Algorithm for multistage stochastic programs with Strongly Convex cost functions

Vincent Guigues (vincent.guigues***at***gmail.com)

Abstract: We introduce DASC, a decomposition method akin to Stochastic Dual Dynamic Programming (SDDP) which solves some multistage stochastic optimization problems having strongly convex cost functions. Similarly to SDDP, DASC approximates cost-to-go functions by a maximum of lower bounding functions called cuts. However, contrary to SDDP, the cuts computed with DASC are quadratic functions. We also prove the convergence of DASC.

Keywords: Strongly convex value function; Monte-Carlo sampling; Stochastic Programming; SDDP

Category 1: Stochastic Programming


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Entry Submitted: 11/09/2017
Entry Accepted: 11/13/2017
Entry Last Modified: 11/17/2017

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