Configuring Mixed-Integer Programming Solvers for Large-Scale Instances

Robin Kemminer (), Jannick Lange (), Jens Peter Kempkes (), Kevin Tierney () and Dimitri Weiß ()
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Robin Kemminer: OPTANO GmbH
Jannick Lange: OPTANO GmbH
Jens Peter Kempkes: OPTANO GmbH
Kevin Tierney: Bielefeld University
Dimitri Weiß: Bielefeld University

SN Operations Research Forum, 2024, vol. 5, issue 2, 1-14

Abstract: Abstract Algorithm configuration techniques automatically search for parameters of solvers and algorithms that provide minimal runtime or maximal solution quality on specified instance sets. Mixed-integer programming (MIP) solvers pose a particular challenge for algorithm configurators due to the difficulty of finding optimal, or even feasible, solutions on the large-scale problems commonly found in practice. We introduce the OPTANO Algorithm Tuner (OAT) to find configurations for MIP solvers and other optimization algorithms. We present and evaluate several critical components of OAT for solving MIPs in particular and show that OAT can find configurations that significantly improve the solution time of MIPs on two different datasets.

Keywords: Algorithm configuration; Mixed-integer programming; Large-scale problem instances (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s43069-024-00327-7

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