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The SCIP Optimization Suite 3.2

Gerald Gamrath(gamrath***at***zib.de)
Tobias Fischer(tfischer***at***mathematik.tu-darmstadt.de)
Tristan Gally(gally***at***mathematik.tu-darmstadt.de)
Ambros M. Gleixner(gleixner***at***zib.de)
Gregor Hendel(hendel***at***zib.de)
Thorsten Koch(koch***at***zib.de)
Stephen J. Maher(maher***at***zib.de)
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)
Sebastian Schenker(schenker***at***zib.de)
Robert Schwarz(schwarz***at***zib.de)
Felipe Serrano(serrano***at***zib.de)
Yuji Shinano(shinano***at***zib.de)
Stefan Vigerske(svigerske***at***gams.com)
Dieter Weninger(dieter.weninger***at***math.uni-erlangen.de)
Michael Winkler(winkler***at***gurobi.com)
Jonas T. Witt(witt***at***or.rwth-aachen.de)
Jakob Witzig(witzig***at***zib.de)

Abstract: The SCIP Optimization Suite is a software toolbox for generating and solving various classes of mathematical optimization problems. Its major components are the modeling language ZIMPL, the linear programming solver SoPlex, the constraint integer programming framework and mixed-integer linear and nonlinear programming solver SCIP, the UG framework for parallelization of branch-and-bound-based solvers, and the generic branch-cut-and-price solver GCG. It has been used in many applications from both academia and industry and is one of the leading non-commercial solvers. This paper highlights the new features of version 3.2 of the SCIP Optimization Suite. Version 3.2 was released in July 2015. This release comes with new presolving steps, primal heuristics, and branching rules within SCIP. In addition, version 3.2 includes a reoptimization feature and improved handling of quadratic constraints and special ordered sets. SoPlex can now solve LPs exactly over the rational number and performance improvements have been achieved by exploiting sparsity in more situations. UG has been tested successfully on 80,000 cores. A major new feature of UG is the functionality to parallelize a customized SCIP solver. GCG has been enhanced with a new separator, new primal heuristics, and improved column management. Finally, new and improved extensions of SCIP are presented, namely solvers for multi-criteria optimization, Steiner tree problems, and mixed-integer semidefinite programs.

Keywords: LP solver; MINLP solver; MIP solver; Steiner tree solver; branch-cut-and-price framework; generic column generation; linear programming; mixed-integer linear and nonlinear programming; mixed-integer semidefinite programming; modeling; multi-criteria optimization; parallel branch-and-bound; simplex method

Category 1: Integer Programming

Category 2: Linear, Cone and Semidefinite Programming

Category 3: Optimization Software and Modeling Systems

Citation: ZR 15-60, Zuse Institute Berlin, 02/2016

Download: [PDF]

Entry Submitted: 03/09/2016
Entry Accepted: 03/09/2016
Entry Last Modified: 03/09/2016

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