On the Performance of NLP Solvers Within Global MINLP Solvers

Benjamin Müller (), Renke Kuhlmann () and Stefan Vigerske ()
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Benjamin Müller: Zuse Institute Berlin
Renke Kuhlmann: University Bremen
Stefan Vigerske: GAMS Software GmbH, c/o Zuse Institute Berlin

A chapter in Operations Research Proceedings 2017, 2018, pp 633-639 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-319-89920-6_84

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DOI: 10.1007/978-3-319-89920-6_84

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