On the performance of NLP solvers within global MINLP solvers

  • 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.

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Metadaten
Author:Benjamin MüllerORCiD, Renke Kuhlmann, Stefan Vigerske
Document Type:In Proceedings
Parent Title (English):Operations Research Proceedings 2017
First Page:633
Last Page:639
Publisher:Springer International Publishing
Year of first publication:2018
Pagenumber:6
Preprint:urn:nbn:de:0297-zib-64504
DOI:https://doi.org/10.1007/978-3-319-89920-6_84