On Generating Lagrangian Cuts for Two-Stage Stochastic Integer Programs

Rui Chen () and James Luedtke ()
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Rui Chen: Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706
James Luedtke: Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706

INFORMS Journal on Computing, 2022, vol. 34, issue 4, 2332-2349

Abstract: We investigate new methods for generating Lagrangian cuts to solve two-stage stochastic integer programs. Lagrangian cuts can be added to a Benders reformulation and are derived from solving single scenario integer programming subproblems identical to those used in the nonanticipative Lagrangian dual of a stochastic integer program. Although Lagrangian cuts have the potential to significantly strengthen the Benders relaxation, generating Lagrangian cuts can be computationally demanding. We investigate new techniques for generating Lagrangian cuts with the goal of obtaining methods that provide significant improvements to the Benders relaxation quickly. Computational results demonstrate that our proposed method improves the Benders relaxation significantly faster than previous methods for generating Lagrangian cuts and, when used within a branch-and-cut algorithm, significantly reduces the size of the search tree for three classes of test problems.

Keywords: two-stage stochastic integer programs; Lagrangian cuts; dual decomposition (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:34:y:2022:i:4:p:2332-2349

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