Optimization Online


The SCIP Optimization Suite 5.0

Ambros Gleixner (gleixner***at***zib.de)
Leon Eifler (eifler***at***zib.de)
Tristan Gally (gally***at***mathematik.tu-darmstadt.de)
Gerald Gamrath (gamrath***at***zib.de)
Patrick Gemander (patrick.gemander***at***fau.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)
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)
Felipe Serrano (serrano***at***zib.de)
Yuji Shinano (shinano***at***zib.de)
Jan Merlin Viernickel (viernickel***at***zib.de)
Stefan Vigerske (vigerske***at***zib.de)
Dieter Weninger (dieter.weninger***at***fau.de)
Jonas Witt (witt***at***or.rwth-aachen.de)
Jakob Witzig (witzig***at***zib.de)

Abstract: This article describes new features and enhanced algorithms made available in version 5.0 of the SCIP Optimization Suite. In its central component, the constraint integer programming solver SCIP, remarkable performance improvements have been achieved for solving mixed-integer linear and nonlinear programs. On MIPs, SCIP 5.0 is about 41 % faster than SCIP 4.0 and over twice as fast on instances that take at least 100 seconds to solve. For MINLP, SCIP 5.0 is about 17 % faster overall and 23 % faster on instances that take at least 100 seconds to solve. This boost is due to algorithmic advances in several parts of the solver such as cutting plane generation and management, a new adaptive coordination of large neighborhood search heuristics, symmetry handling, and strengthened McCormick relaxations for bilinear terms in MINLPs. Besides discussing the theoretical background and the implementational aspects of these developments, the report describes recent additions for the other software packages connected to SCIP, in particular for the LP solver SoPlex, the Steiner tree solver SCIP-Jack, the MISDP solver SCIP-SDP, and the parallelization framework UG.

Keywords: constraint integer programming, linear programming, mixed-integer linear programming, mixed-integer nonlinear programming, optimization solver, branch-and-cut, branch-and-price, column generation framework, 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 17-61, Zuse Institute Berlin, Takustr 7, 14195 Berlin, Germany

Download: [PDF]

Entry Submitted: 12/21/2017
Entry Accepted: 12/21/2017
Entry Last Modified: 12/29/2017

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