Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/308744 
Year of Publication: 
2023
Citation: 
[Journal:] Journal of Global Optimization [ISSN:] 1573-2916 [Publisher:] Springer US [Place:] New York, NY [Year:] 2023 [Pages:] 1-24
Publisher: 
Springer US, New York, NY
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.
Subjects: 
Global optimization
Mixed-integer nonlinear programming
SCIP
Branch-and-cut
Optimization software
Benchmark
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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