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The SCIP Optimization Suite 7.0

Gerald Gamrath(gamrath***at***zib.de)
Daniel Anderson(dlanders***at***cs.cmu.edu)
Ksenia Bestuzheva(bestuzheva***at***zib.de)
Wei-Kun Chen(chenweikun***at***bit.edu.cn)
Leon Eifler(eifler***at***zib.de)
Maxim Gasse(maxime.gasse***at***polymtl.ca)
Patrick Gemander(patrick.gemander***at***fau.de)
Ambros Gleixner(gleixner***at***zib.de)
Leona Gottwald(gottwald***at***zib.de)
Katrin Halbig(katrin.halbig***at***fau.de)
Gregor Hendel(hendel***at***zib.de)
Christopher Hojny(c.hojny***at***tue.nl)
Thorsten Koch(koch***at***zib.de)
Pierre Le Bodic(pierre.lebodic***at***monash.edu)
Stephen J. Maher(s.j.maher***at***exeter.ac.uk)
Frederic Matter(matter***at***mathematik.tu-darmstadt.de)
Matthias Miltenberger(miltenberger***at***zib.de)
Erik Mühmer(muehmer***at***or.rwth-aachen.de)
Benjamin Müller(benjamin.mueller***at***zib.de)
Marc E. Pfetsch(pfetsch***at***mathematik.tu-darmstadt.de)
Franziska Schlösser(schloesser***at***zib.de)
Felipe Serrano(serrano***at***zib.de)
Shinano Yuji(shinano***at***zib.de)
Christine Tawfik(tawfik***at***zib.de)
Stefan Vigerske(svigerske***at***gams.com)
Fabian Wegscheider(wegscheider***at***zib.de)
Dieter Weninger(dieter.weninger***at***fau.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 7.0 of the SCIP Optimization Suite. The new version features the parallel presolving library PaPILO as a new addition to the suite. PaPILO 1.0 simplifies mixed-integer linear optimization problems and can be used stand-alone or integrated into SCIP via a presolver plugin. SCIP 7.0 provides additional support for decomposition algorithms. Besides improvements in the Benders’ decomposition solver of SCIP, user-defined decomposition structures can be read, which are used by the automated Benders’ decomposition solver and two primal heuristics. Additionally, SCIP 7.0 comes with a tree size estimation that is used to predict the completion of the overall solving process and potentially trigger restarts. Moreover, substantial performance improvements of the MIP core were achieved by new developments in presolving, primal heuristics, branching rules, conflict analysis, and symmetry handling. Last, not least, the report presents updates to other components and extensions of the SCIP Optimization Suite, in particular, the LP solver SoPlex 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, Presolving, Tree Size Estimation

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

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

Citation: ZIB-Report 20-10, Zuse Institute Berlin, Takustr 7, 14195 Berlin, Germany

Download: [PDF]

Entry Submitted: 03/30/2020
Entry Accepted: 03/30/2020
Entry Last Modified: 03/30/2020

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