Download PDFOpen PDF in browserSolving stochastic programming problems with randomized scenario samplingEasyChair Preprint 28462 pages•Date: March 3, 2020AbstractMultistage Stochastic Programming consists in minimizing the expected cost of some decision over a set of coupled scenarios. Progressive Hedging is a popular strategy for solving such problems, based on scenario decomposition. In this talk, we present a randomized version of this algorithm able to compute an update as soon as a scenario subproblem is solved. This is of crucial importance when run on parallel computing architectures. We prove that the randomized version has the same converge properties as the standard one and we release an easy-to-use Julia toolbox. Keyphrases: Julia, Progressive Hedging, multistage stochastic programming
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