Progressive Hedging (PH) is a well-known algorithm for solving multi-stage stochastic convex optimization problems. Most previous extensions of PH for stochastic mixed-integer programs have been implemented without convergence guarantees. In this paper, we present a new framework that shows how PH can be utilized while guaranteeing convergence to globally optimal solutions of stochastic mixed-integer convex programs. We demonstrate the effectiveness of the proposed framework through computational experiments.
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University of Southern California.
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View A Progressive Hedging Based Branch-and-Bound Algorithm for Stochastic Mixed-Integer Programs