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The SCIP Optimization Suite 6.0

Please always quote using this URN: urn:nbn:de:0297-zib-69361
  • The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. This paper discusses enhancements and extensions contained in version 6.0 of the SCIP Optimization Suite. Besides performance improvements of the MIP and MINLP core achieved by new primal heuristics and a new selection criterion for cutting planes, one focus of this release are decomposition algorithms. Both SCIP and the automatic decomposition solver GCG now include advanced functionality for performing Benders’ decomposition in a generic framework. GCG’s detection loop for structured matrices and the coordination of pricing routines for Dantzig-Wolfe decomposition has been significantly revised for greater flexibility. Two SCIP extensions have been added to solve the recursive circle packing problem by a problem-specific column generation scheme and to demonstrate the use of the new Benders’ framework for stochastic capacitated facility location. Last, not least, the report presents updates and additions to the other components and extensions of the SCIP Optimization Suite: the LP solver SoPlex, the modeling language Zimpl, the parallelization framework UG, the Steiner tree solver SCIP-Jack, and the mixed-integer semidefinite programming solver SCIP-SDP.

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Author:Ambros GleixnerORCiD, Michael Bastubbe, Leon EiflerORCiD, Tristan Gally, Gerald GamrathORCiD, Robert Lion GottwaldORCiD, Gregor HendelORCiD, Christopher Hojny, Thorsten KochORCiD, Marco Lübbecke, Stephen J. Maher, Matthias MiltenbergerORCiD, Benjamin MüllerORCiD, Marc PfetschORCiD, Christian Puchert, Daniel RehfeldtORCiD, Franziska SchlösserORCiD, Christoph Schubert, Felipe SerranoORCiD, Yuji ShinanoORCiD, Jan Merlin Viernickel, Matthias Walter, Fabian Wegscheider, Jonas T. Witt, Jakob WitzigORCiD
Document Type:ZIB-Report
Tag:Steiner tree optimization; branch-and-cut; branch-and-price; column generation framework; constraint integer programming; linear programming; mixed-integer linear programming; mixed-integer nonlinear programming; mixed-integer semidefinite programming; optimization solver; parallelization
Date of first Publication:2018/07/02
Series (Serial Number):ZIB-Report (18-26)
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