Overview Statistic: PDF-Downloads (blue) and Frontdoor-Views (gray)

SCIP: Global Optimization of Mixed-Integer Nonlinear Programs in a Branch-and-Cut Framework

  • This paper describes the extensions that were added to the constraint integer programming framework SCIP in order to enable it to solve convex and nonconvex mixed-integer nonlinear programs (MINLPs) to global optimality. SCIP implements a spatial branch-and-bound algorithm based on a linear outer-approximation, which is computed by convex over- and underestimation of nonconvex functions. An expression graph representation of nonlinear constraints allows for bound tightening, structure analysis, and reformulation. Primal heuristics are employed throughout the solving process to find feasible solutions early. We provide insights into the performance impact of individual MINLP solver components via a detailed computational study over a large and heterogeneous test set.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics - number of accesses to the document
Metadaten
Author:Stefan Vigerske, Ambros GleixnerORCiD
Document Type:Article
Parent Title (English):Optimization Methods & Software
Volume:33
Issue:3
First Page:563
Last Page:593
Year of first publication:2018
Preprint:urn:nbn:de:0297-zib-59377
DOI:https://doi.org/10.1080/10556788.2017.1335312
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.