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arXiv · 1702.00074

Combining Penalty-based and Gauss-Seidel Methods for solving Stochastic Mixed-Integer Problems

Fabricio Oliveira·Jeffrey Christiansen·Brian Dandurand·Andrew Eberhard
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Abstract

In this paper, we propose a novel decomposition approach for mixed-integer stochastic programming (SMIP) problems that is inspired by the combination of penalty-based Lagrangian and block Gauss-Seidel methods (PBGS). In this sense, PBGS is developed such that the inherent decomposable structure that SMIPs present can be exploited in a computationally efficient manner. The performance of the proposed method is compared with the Progressive Hedging method (PH), which also can be viewed as a Lagrangian-based method for obtaining solutions for SMIP. Numerical experiments performed using instances from the literature illustrate the efficiency of the proposed method in terms of computational performance and solution quality.

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Fabricio Oliveira, Jeffrey Christiansen, Brian Dandurand, Andrew Eberhard. 2017-01-31. Combining Penalty-based and Gauss-Seidel Methods for solving Stochastic Mixed-Integer Problems. https://doi.org/10.1111/itor.12525

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