6217
eng
reportzib
0
--
2017-08-03
--
The SCIP Optimization Suite 4.0
The SCIP Optimization Suite is a powerful collection of optimization software that consists of the branch-cut-and-price framework and mixed-integer programming solver SCIP, the linear programming solver SoPlex, the modeling language Zimpl, the parallelization framework UG, and the generic branch-cut-and-price solver GCG. Additionally, it features the extensions SCIP-Jack for solving Steiner tree problems, PolySCIP for solving multi-objective problems, and SCIP-SDP for solving mixed-integer semidefinite programs. The SCIP Optimization Suite has been continuously developed and has now reached version 4.0. The goal of this report is to present the recent changes to the collection. We not only describe the theoretical basis, but focus on implementation aspects and their computational consequences.
1438-0064
urn:nbn:de:0297-zib-62170
Stephen J. Maher
Ambros Gleixner
Tobias Fischer
Tristan Gally
Gerald Gamrath
Ambros Gleixner
Robert Lion Gottwald
Gregor Hendel
Thorsten Koch
Marco Lübbecke
Matthias Miltenberger
Benjamin Müller
Marc Pfetsch
Christian Puchert
Daniel Rehfeldt
Sebastian Schenker
Robert Schwarz
Felipe Serrano
Yuji Shinano
Dieter Weninger
Jonas T. Witt
Jakob Witzig
ZIB-Report
17-12
Computer aspects of numerical algorithms
Linear programming
Mixed integer programming
Nonconvex programming, global optimization
Mathematical Optimization
Mathematical Optimization Methods
Gamrath, Gerald
Gleixner, Ambros
Gottwald, Robert
Hendel, Gregor
Koch, Thorsten
Miltenberger, Matthias
Rehfeldt, Daniel
Serrano, Felipe
Shinano, Yuji
Müller, Benjamin
ASTfSCM
MIP-ZIBOPT
MODAL-SynLab
Siemens
MODAL-Gesamt
https://opus4.kobv.de/opus4-zib/files/6217/scipoptsuite-40.pdf
https://opus4.kobv.de/opus4-zib/files/6217/scipoptsuite-401.pdf
5767
eng
reportzib
0
--
2016-02-26
--
The SCIP Optimization Suite 3.2
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.
1438-0064
urn:nbn:de:0297-zib-57675
no
Gerald Gamrath
Gerald Gamrath
Tobias Fischer
Tristan Gally
Ambros Gleixner
Gregor Hendel
Thorsten Koch
Stephen J. Maher
Matthias Miltenberger
Benjamin Müller
Marc Pfetsch
Christian Puchert
Daniel Rehfeldt
Sebastian Schenker
Robert Schwarz
Felipe Serrano
Yuji Shinano
Stefan Vigerske
Dieter Weninger
Michael Winkler
Jonas T. Witt
Jakob Witzig
ZIB-Report
15-60
eng
uncontrolled
mixed-integer linear and nonlinear programming
eng
uncontrolled
MIP solver
eng
uncontrolled
MINLP solver
eng
uncontrolled
linear programming
eng
uncontrolled
LP solver
eng
uncontrolled
simplex method
eng
uncontrolled
modeling
eng
uncontrolled
parallel branch-and-bound
eng
uncontrolled
branch-cut-and-price framework
eng
uncontrolled
generic column generation
eng
uncontrolled
Steiner tree solver
eng
uncontrolled
multi-criteria optimization
eng
uncontrolled
mixed-integer semidefinite programming
Parallel computation
Linear programming
Integer programming
Mixed integer programming
Nonlinear programming
Applications of mathematical programming
Mathematical Optimization
Mathematical Optimization Methods
Gamrath, Gerald
Gleixner, Ambros
Hendel, Gregor
Koch, Thorsten
Miltenberger, Matthias
Rehfeldt, Daniel
Serrano, Felipe
Shinano, Yuji
Vigerske, Stefan
Winkler, Michael
Müller, Benjamin
ASTfSCM
MIP-ZIBOPT
MODAL-SynLab
CRC1026
Siemens
MODAL-Gesamt
https://opus4.kobv.de/opus4-zib/files/5767/scipopt-32.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