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Configuring Mixed-Integer Programming Solvers for Large-Scale Instances

Author

Listed:
  • Robin Kemminer

    (OPTANO GmbH)

  • Jannick Lange

    (OPTANO GmbH)

  • Jens Peter Kempkes

    (OPTANO GmbH)

  • Kevin Tierney

    (Bielefeld University)

  • Dimitri Weiß

    (Bielefeld University)

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.

Suggested Citation

  • Robin Kemminer & Jannick Lange & Jens Peter Kempkes & Kevin Tierney & Dimitri Weiß, 2024. "Configuring Mixed-Integer Programming Solvers for Large-Scale Instances," SN Operations Research Forum, Springer, vol. 5(2), pages 1-14, June.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:2:d:10.1007_s43069-024-00327-7
    DOI: 10.1007/s43069-024-00327-7
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    References listed on IDEAS

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    1. Belarmino Adenso-Díaz & Manuel Laguna, 2006. "Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search," Operations Research, INFORMS, vol. 54(1), pages 99-114, February.
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    3. Liberto, Giovanni Di & Kadioglu, Serdar & Leo, Kevin & Malitsky, Yuri, 2016. "DASH: Dynamic Approach for Switching Heuristics," European Journal of Operational Research, Elsevier, vol. 248(3), pages 943-953.
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