Global optimization of mixed-integer nonlinear programs with SCIP 8
Ksenia Bestuzheva, Antonia Chmiela, Benjamin Müller, Felipe Serrano, Stefan Vigerske, Fabian Wegscheider
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.
Topics & Concepts
Integer programmingInteger (computer science)Global optimizationMathematical optimizationMathematicsBenchmarkingNonlinear programmingConstraint (computer-aided design)State (computer science)Nonlinear systemRegular polygonLinear programmingConstraint programmingAlgorithmComputer scienceStochastic programmingProgramming languageGeometryBusinessPhysicsQuantum mechanicsMarketingAdvanced Optimization Algorithms ResearchAdvanced Control Systems OptimizationOptimization and Mathematical Programming