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On the Performance of NLP Solvers Within Global MINLP Solvers

  • Benjamin Müller
  • Renke Kuhlmann
  • Stefan Vigerske
Conference paper
Part of the Operations Research Proceedings book series (ORP)

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.

Keywords

Mixed-integer nonlinear programming Interior point Sequential quadratic programming Global optimization 

Notes

Acknowledgements

This work has been supported by the Research Campus MODAL Mathematical Optimization and Data Analysis Laboratories funded by the Federal Ministry of Education and Research (BMBF Grant 05M14ZAM).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Benjamin Müller
    • 1
  • Renke Kuhlmann
    • 2
  • Stefan Vigerske
    • 3
  1. 1.Zuse Institute BerlinBerlinGermany
  2. 2.University BremenBremenGermany
  3. 3.GAMS Software GmbH, c/o Zuse Institute BerlinBerlinGermany

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