6138
eng
reportzib
0
--
2016-12-07
--
Distributed domain propagation
Portfolio parallelization is an approach that runs several solver instances in parallel and terminates when one of them succeeds in solving the problem. Despite it's simplicity portfolio parallelization has been shown to perform well for modern mixed-integer programming (MIP) and boolean satisfiability problem (SAT) solvers. Domain propagation has also been shown to be a simple technique in modern MIP and SAT solvers that effectively finds additional domain reductions after a variables domain has been reduced. This paper investigates the impact of distributed domain propagation in modern MIP solvers that employ portfolio parallelization. Computational experiments were conducted for two implementations of this parallelization approach. While both share global variable bounds and solutions they communicate differently. In one implementation the communication is performed only at designated points in the solving process and in the other it is performed completely asynchronously. Computational experiments show a positive performance impact of communicating global variable bounds and provide valuable insights in communication strategies for parallel solvers.
1438-0064
urn:nbn:de:0297-zib-61380
10.4230/LIPIcs.SEA.2017.6
16th International Symposium on Experimental Algorithms (SEA 2017). Leibniz International Proceedings in Informatics, Volume 75, SEA 2017, June 21–23, 2017 - London, UK Costas S. Iliopoulos and Solon P. Pissis and Simon J. Puglisi and Rajeev Raman (Eds.)
Robert Lion Gottwald
Robert Lion Gottwald
Stephen J. Maher
Yuji Shinano
ZIB-Report
16-71
eng
uncontrolled
mixed integer programming
eng
uncontrolled
parallelization
eng
uncontrolled
domain propagation
eng
uncontrolled
portfolio solvers
Mathematics of Computing
OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
Mathematical Optimization
Mathematical Optimization Methods
Gottwald, Robert
Shinano, Yuji
ASTfSCM
MIP-ZIBOPT
MODAL-SynLab
Siemens
MODAL-Gesamt
https://opus4.kobv.de/opus4-zib/files/6138/16-71.pdf
6629
eng
reportzib
0
--
2017-12-21
--
The SCIP Optimization Suite 5.0
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.
1438-0064
urn:nbn:de:0297-zib-66297
Ambros Gleixner
Gregor Hendel
Leon Eifler
Tristan Gally
Gerald Gamrath
Patrick Gemander
Robert Lion Gottwald
Gregor Hendel
Christopher Hojny
Thorsten Koch
Matthias Miltenberger
Benjamin Müller
Marc Pfetsch
Christian Puchert
Daniel Rehfeldt
Franziska Schlösser
Felipe Serrano
Yuji Shinano
Jan Merlin Viernickel
Stefan Vigerske
Dieter Weninger
Jonas T. Witt
Jakob Witzig
ZIB-Report
17-61
eng
uncontrolled
constraint integer programming
eng
uncontrolled
linear programming
eng
uncontrolled
mixed-integer linear programming
eng
uncontrolled
mixed-integer nonlinear programming
eng
uncontrolled
optimization solver
eng
uncontrolled
branch-and-cut
eng
uncontrolled
branch-and-price
eng
uncontrolled
column generation framework
eng
uncontrolled
parallelization
eng
uncontrolled
mixed-integer semidefinite programming
eng
uncontrolled
Steiner tree optimization
NUMERICAL ANALYSIS
OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
Mathematical Optimization
Mathematical Optimization Methods
Gamrath, Gerald
Gleixner, Ambros
Gottwald, Robert
Hendel, Gregor
Koch, Thorsten
Miltenberger, Matthias
Rehfeldt, Daniel
Serrano, Felipe
Shinano, Yuji
Vigerske, Stefan
Müller, Benjamin
ASTfSCM
MIP-ZIBOPT
MODAL-SynLab
Siemens
Eifler, Leon
MODAL-Gesamt
Schlösser, Franziska
https://opus4.kobv.de/opus4-zib/files/6629/scipopt-50.pdf
6936
eng
reportzib
0
--
2018-07-02
--
The SCIP Optimization Suite 6.0
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.
1438-0064
urn:nbn:de:0297-zib-69361
Matthias Miltenberger
Ambros Gleixner
Michael Bastubbe
Leon Eifler
Tristan Gally
Gerald Gamrath
Robert Lion Gottwald
Gregor Hendel
Christopher Hojny
Thorsten Koch
Marco Lübbecke
Stephen J. Maher
Matthias Miltenberger
Benjamin Müller
Marc Pfetsch
Christian Puchert
Daniel Rehfeldt
Franziska Schlösser
Christoph Schubert
Felipe Serrano
Yuji Shinano
Jan Merlin Viernickel
Matthias Walter
Fabian Wegscheider
Jonas T. Witt
Jakob Witzig
ZIB-Report
18-26
eng
uncontrolled
constraint integer programming
eng
uncontrolled
linear programming
eng
uncontrolled
mixed-integer linear programming
eng
uncontrolled
mixed-integer nonlinear programming
eng
uncontrolled
optimization solver
eng
uncontrolled
branch-and-cut
eng
uncontrolled
branch-and-price
eng
uncontrolled
column generation framework
eng
uncontrolled
parallelization
eng
uncontrolled
mixed-integer semidefinite programming
eng
uncontrolled
Steiner tree optimization
NUMERICAL ANALYSIS
OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING
Mathematical Optimization
Mathematical Optimization Methods
Gamrath, Gerald
Gleixner, Ambros
Gottwald, Robert
Hendel, Gregor
Koch, Thorsten
Miltenberger, Matthias
Rehfeldt, Daniel
Serrano, Felipe
Shinano, Yuji
Vigerske, Stefan
Müller, Benjamin
ASTfSCM
MIP-ZIBOPT
MODAL-SynLab
Siemens
Eifler, Leon
MODAL-Gesamt
Schlösser, Franziska
Plan4res
https://opus4.kobv.de/opus4-zib/files/6936/scipopt-60.pdf