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A Progressive Hedging Based Branch-and-Bound Algorithm for Stochastic Mixed-Integer Programs

Semih Atakan (atakan***at***usc.edu)
Suvrajeet Sen (s.sen***at***usc.edu)

Abstract: 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.

Keywords: multi-stage stochastic mixed-integer convex programming; progressive hedging

Category 1: Stochastic Programming

Citation: University of Southern California.

Download: [PDF]

Entry Submitted: 05/13/2017
Entry Accepted: 05/14/2017
Entry Last Modified: 05/15/2017

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