Computational aspects of infeasibility analysis in mixed integer programming

  • The analysis of infeasible subproblems plays an important role in solving mixed integer programs (MIPs) and is implemented in most major MIP solvers. There are two fundamentally different concepts to generate valid global constraints from infeasible subproblems: conflict graph analysis and dual proof analysis. While conflict graph analysis detects sets of contradicting variable bounds in an implication graph, dual proof analysis derives valid linear constraints from the proof of the dual LP’s unboundedness. The main contribution of this paper is twofold. Firstly, we present three enhancements of dual proof analysis: presolving via variable cancellation, strengthening by applying mixed integer rounding functions, and a filtering mechanism. Further, we provide a comprehensive computational study evaluating the impact of every presented component regarding dual proof analysis. Secondly, this paper presents the first combined approach that uses both conflict graph and dual proof analysis simultaneously within a single MIP solution process. All experiments are carried out on general MIP instances from the standard public test set Miplib  2017; the presented algorithms have been implemented within the non-commercial MIP solver and the commercial MIP solver .

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Metadaten
Author:Jakob WitzigORCiD, Timo BertholdORCiD, Stefan Heinz
DOI:https://doi.org/10.1007/s12532-021-00202-0
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2021
Volume:13
Issue:4
Page Number:33
First Page:753
Last Page:785
Mathematical Programming Computation :MPC 2021 - Issue 4
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