Global optimization of mixed-integer nonlinear programs with SCIP 8

Ksenia Bestuzheva (), Antonia Chmiela (), Benjamin Müller (), Felipe Serrano (), Stefan Vigerske () and Fabian Wegscheider
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Ksenia Bestuzheva: Zuse Institute Berlin
Antonia Chmiela: Zuse Institute Berlin
Benjamin Müller: Zuse Institute Berlin
Felipe Serrano: Zuse Institute Berlin
Stefan Vigerske: c/o Zuse Institute Berlin
Fabian Wegscheider: Zuse Institute Berlin

Journal of Global Optimization, 2025, vol. 91, issue 2, No 3, 287-310

Abstract: Abstract For over 10 years, the constraint integer programming framework SCIP has been extended by capabilities for the solution of convex and nonconvex mixed-integer nonlinear programs (MINLPs). With the recently published version 8.0, these capabilities have been largely reworked and extended. This paper discusses the motivations for recent changes and provides an overview of features that are particular to MINLP solving in SCIP. Further, difficulties in benchmarking global MINLP solvers are discussed and a comparison with several state-of-the-art global MINLP solvers is provided.

Keywords: Global optimization; Mixed-integer nonlinear programming; SCIP; Branch-and-cut; Optimization software; Benchmark; 65K05; 90-08; 90C11; 90C20; 90C26 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10898-023-01345-1

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