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The SCIP Optimization Suite 6.0

Ambros Gleixner (gleixner***at***zib.de)
Michael Bastubbe (bastubbe***at***or.rwth-aachen.de)
Leon Eifler (eifler***at***zib.de)
Tristan Gally (gally***at***mathematik.tu-darmstadt.de)
Gerald Gamrath (gamrath***at***zib.de)
Robert Lion Gottwald (robert.gottwald***at***zib.de)
Gregor Hendel (hendel***at***zib.de)
Christopher Hojny (hojny***at***mathematik.tu-darmstadt.de)
Thorsten Koch (koch***at***zib.de)
Marco E. Lübbecke (luebbecke***at***or.rwth-aachen.de)
Stephen J. Maher (s.maher3***at***lancaster.ac.uk)
Matthias Miltenberger (miltenberger***at***zib.de)
Benjamin Müller (benjamin.mueller***at***zib.de)
Marc E. Pfetsch (pfetsch***at***mathematik.tu-darmstadt.de)
Christian Puchert (puchert***at***or.rwth-aachen.de)
Daniel Rehfeldt (rehfeldt***at***zib.de)
Franziska Schlösser (schloesser***at***zib.de)
Christoph Schubert (schubert***at***zib.de)
Felipe Serrano (serrano***at***zib.de)
Yuji Shinano (shinano***at***zib.de)
Jan Merlin Viernickel (viernickel***at***zib.de)
Matthias Walter (witt***at***or.rwth-aachen.de)
Fabian Wegscheider (wegscheider***at***zib.de)
Jonas T. Witt (witt***at***or.rwth-aachen.de)
Jakob Witzig (witzig***at***zib.de)

Abstract: 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.

Keywords: Constraint integer programming, linear programming, mixed-integer linear programming, mixed-integer nonlinear programming, optimization solver, branch- and-cut, branch-and-price, column generation, Benders’ decomposition, parallelization, mixed-integer semidefinite programming, Steiner tree optimization

Category 1: Integer Programming ((Mixed) Integer Linear Programming )

Category 2: Integer Programming ((Mixed) Integer Nonlinear Programming )

Citation: ZIB-Report 18-26, Zuse Institute Berlin, Takustr 7, 14195 Berlin, Germany

Download: [PDF]

Entry Submitted: 07/02/2018
Entry Accepted: 07/02/2018
Entry Last Modified: 07/03/2018

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