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-ap...

Full description

Saved in:
Bibliographic Details
Published inOptimization methods & software Vol. 33; no. 3; pp. 563 - 593
Main Authors Vigerske, Stefan, Gleixner, Ambros
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis Ltd 04.05.2018
Subjects
Branch & bound algorithms
Global optimization
Graph representations
Graphical representations
Integer programming
Structural analysis
Online AccessGet full text
ISSN1055-6788
1029-4937
DOI10.1080/10556788.2017.1335312

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1055-6788
1029-4937
DOI:10.1080/10556788.2017.1335312