Solving stochastic programming problems with randomized scenario sampling

EasyChair Preprint 2846

Abstract

Multistage 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

@booklet{EasyChair:2846,
  author    = {Gilles Bareilles and Dmitry Grishchenko and Franck Iutzeler and Yassine Laguel and Jérôme Malick},
  title     = {Solving stochastic programming problems with randomized scenario sampling},
  howpublished = {EasyChair Preprint 2846},
  year      = {EasyChair, 2020}}