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On the Performance of NLP Solvers Within Global MINLP Solvers

In: Operations Research Proceedings 2017

Author

Listed:
  • Benjamin Müller

    (Zuse Institute Berlin)

  • Renke Kuhlmann

    (University Bremen)

  • Stefan Vigerske

    (GAMS Software GmbH, c/o Zuse Institute Berlin)

Abstract

Solving mixed-integer nonlinear programs (MINLPs) to global optimality efficiently requires fast solvers for continuous sub-problems. These appear in, e.g., primal heuristics, convex relaxations, and bound tightening methods. Two of the best performing algorithms for these sub-problems are Sequential Quadratic Programming (SQP) and Interior Point Methods. In this paper we study the impact of different SQP and Interior Point implementations on important MINLP solver components that solve a sequence of similar NLPs. We use the constraint integer programming framework SCIP for our computational studies.

Suggested Citation

  • Benjamin Müller & Renke Kuhlmann & Stefan Vigerske, 2018. "On the Performance of NLP Solvers Within Global MINLP Solvers," Operations Research Proceedings, in: Natalia Kliewer & Jan Fabian Ehmke & Ralf Borndörfer (ed.), Operations Research Proceedings 2017, pages 633-639, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-89920-6_84
    DOI: 10.1007/978-3-319-89920-6_84
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