A Progressive Hedging based branch-and-bound algorithm for mixed-integer stochastic programs

A Progressive Hedging based branch-and-bound algorithm for mixed-integer stochastic programs Progressive Hedging (PH) is a well-known algorithm for solving multi-stage stochastic convex optimization problems. Most previous extensions of PH for mixed-integer stochastic 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 mixed-integer stochastic convex programs. We demonstrate the effectiveness of the proposed framework through computational experiments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computational Management Science Springer Journals

A Progressive Hedging based branch-and-bound algorithm for mixed-integer stochastic programs

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Business and Management; Operations Research/Decision Theory; Optimization
ISSN
1619-697X
eISSN
1619-6988
D.O.I.
10.1007/s10287-018-0311-3
Publisher site
See Article on Publisher Site

Abstract

Progressive Hedging (PH) is a well-known algorithm for solving multi-stage stochastic convex optimization problems. Most previous extensions of PH for mixed-integer stochastic 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 mixed-integer stochastic convex programs. We demonstrate the effectiveness of the proposed framework through computational experiments.

Journal

Computational Management ScienceSpringer Journals

Published: Jun 5, 2018

References

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