An ADMM algorithm for two-stage stochastic programming problems
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The alternate direction method of multipliers (ADMM) has received significant attention recently as a powerful algorithm to solve convex problems with a block structure. The vast majority of applications focus on deterministic problems. In this paper we show that ADMM can be applied to solve two-stage stochastic programming problems, and we propose an implementation in three blocks with or without proximal terms. We present numerical results for large scale instances, and extend our findings for risk averse formulations using utility functions.
The authors thank two anonymous referees for their constructive comments, which in particular allowed us to provide a generic formulation of the algorithm that includes the cases with and without proximal terms. This research was supported by FONDECYT Project No. 1171145.
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