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

Semih Atakan () and Suvrajeet Sen ()
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Semih Atakan: University of Southern California
Suvrajeet Sen: University of Southern California

Computational Management Science, 2018, vol. 15, issue 3, No 10, 540 pages

Abstract: 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.

Keywords: Multi-stage mixed-integer stochastic convex programming; Progressive Hedging; Branch-and-bound (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10287-018-0311-3

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