SCIP is currently one of the fastest non-commercial solvers for mixed integer programming (MIP) and mixed integer nonlinear programming (MINLP). It is also a framework for constraint integer programming and branch-cut-and-price. It allows for total control of the solution process and the access of detailed information down to the guts of the solver.
September/2024 | SCIP version 9.1.1 The SCIP Optimization Suite 9.1.1 consists of SCIP 9.1.1, GCG 3.6.3, SoPlex 7.1.1, ZIMPL 3.6.2, PaPILO 2.3.1 and UG 1.0.0 beta 5. Please check the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
June/2024 | SCIP version 9.1.0 The SCIP Optimization Suite 9.1.0 (minor release) consists of SCIP 9.1.0, GCG 3.6.2, SoPlex 7.1.0, ZIMPL 3.6.1, PaPILO 2.3.0 and UG 1.0.0 beta 5. Please check the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
May/2024 | SCIP version 9.0.1 The SCIP Optimization Suite 9.0.1 consists of SCIP 9.0.1, GCG 3.6.1, SoPlex 7.0.1, ZIMPL 3.6.0, PaPILO 2.2.1 and UG 1.0.0 beta 4. Please check the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
Feb/2024 | SCIP version 9.0.0 The SCIP Optimization Suite 9.0.0 consists of SCIP 9.0.0, GCG 3.6.0, SoPlex 7.0.0, ZIMPL 3.5.3, PaPILO 2.2.0 and UG 1.0.0 beta 4. Please check the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
Nov/2023 | SCIP version 8.1.0 The SCIP Optimization Suite 8.1.0 consists of SCIP 8.1.0, GCG 3.5.5, SoPlex 6.0.4, ZIMPL 3.5.3, PaPILO 2.1.4 and UG 1.0.0 beta 3. Please check the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
Aug/2023 | SCIP version 8.0.4 The SCIP Optimization Suite 8.0.4 consists of SCIP 8.0.4, GCG 3.5.3, SoPlex 6.0.4, ZIMPL 3.5.3, PaPILO 2.1.3 and UG 1.0.0 beta 3. Please check the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
Dec/2022 | SCIP version 8.0.3 The SCIP Optimization Suite 8.0.3 consists of SCIP 8.0.3, GCG 3.5.3, SoPlex 6.0.3, ZIMPL 3.5.3, PaPILO 2.1.2 and UG 1.0.0 beta 3. Please check the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
04/Nov/2022 | SCIP license update Starting with the next release SCIP will be licensed under the Apache 2.0 License. |
06/Oct/2022 | SCIP version 8.0.2 The SCIP Optimization Suite 8.0.2 consists of SCIP 8.0.2, GCG 3.5.2, SoPlex 6.0.2, ZIMPL 3.5.3, PaPILO 2.1.1 and UG 1.0.0 beta 2. Please check the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
Aug/2022 | Announcing the SCIP Workshop 2022 to the vicenary anniversary of SCIP in fall 2022. |
Jun/2022 | SCIP version 8.0.1 The SCIP Optimization Suite 8.0.1 consists of SCIP 8.0.1, GCG 3.5.1, SoPlex 6.0.1, ZIMPL 3.5.2, PaPILO 2.1.0 and UG 1.0.0 beta 2. Please check the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
28/Jan/2022 | SCIP version 8.0.0 The SCIP Optimization Suite 8.0.0 consists of SCIP 8.0.0, GCG 3.5.0, SoPlex 6.0.0, ZIMPL 3.5.1, PaPILO 2.0.0 and UG 1.0.0 beta. Please check the release report on Optimization Online and the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
15/Dec/2021 | Beta of SCIP version 8.0.0 released |
12/Aug/2021 | SCIP version 7.0.3 The version of the SCIP Optimization Suite 7.0.3 consists of SCIP 7.0.3, GCG 3.0.5, SoPlex 5.0.2, ZIMPL 3.4.0, PaPILO 1.0.2 and UG 0.9.1. It contains bugfixes for SCIP and GCG, see the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
5/Jun/2021 | Beta of SCIP version 7.0.3 released |
26/May/2021 | Public Mirrors on GitHub The two main development branches of SCIP, SoPlex and PaPILO are now publicly mirrored on GitHub. |
13/Jan/2021 | SCIP version 7.0.2 The SCIP Optimization Suite 7.0.2 consists of SCIP 7.0.2, SoPlex 5.0.2, ZIMPL 3.4.0, GCG 3.0.4, PaPILO 1.0.2 and UG 0.9.1. It contains important bugfixes and other improvements for all components of the Optimization Suite, see the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
19/Dec/2020 | Beta of SCIP version 7.0.2 released |
25/Sep/2020 | Patched bliss fork now available on GitHub For symmetry detection the SCIPOptSuite now uses a fork of bliss available on GitHub. |
23/Jun/2020 | SCIP version 7.0.1 released The SCIP Optimization Suite 7.0.1 consists of SCIP 7.0.1, SoPlex 5.0.1, ZIMPL 3.4.0, GCG 3.0.3, PaPILO 1.0.1 and UG 0.9.0. It contains important bugfixes and other improvements for all components of the Optimization Suite, see the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
28/May/2020 | Marc Pfetsch and Sebastian Pokutta wrote a blog post about an easy to use dockerized SCIP container for teaching . |
12/May/2020 | Open positions in the development team: 5 years PostDoc (apply here) and 3 years PhD (apply here)! After the underlying funding scheme of Research Campus MODAL has been extended until 2025, ZIB is looking to grow the SCIP team in different research directions for the next years to come. |
20/Apr/2020 | There will be a SCIP Online Workshop on 3rd and 4th of June 2020. |
8/Apr/2020 | SCIP version 7.0.0 packages updated Source code package and windows executables have been updated to resolve an error with TBB. |
30/Mar/2020 | SCIP version 7.0.0 released The SCIP Optimization Suite 7.0.0 consists of SCIP 7.0.0, SoPlex 5.0.0, ZIMPL 3.3.9, GCG 3.0.3, PaPILO 1.0.0 and UG 0.8.9. It contains important bugfixes and other improvements for all components of the Optimization Suite, see the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
10/Jul/2019 | SCIP version 6.0.2 released The SCIP Optimization Suite 6.0.2 consists of SCIP 6.0.2, SoPlex 4.0.2, ZIMPL 3.3.8, GCG 3.0.2, and UG 0.8.8. It contains important bugfixes and other improvements for all components of the Optimization Suite, see the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
28/Jun/2019 | Beta of SCIP version 6.0.2 released |
10/Jan/2019 | SCIP version 6.0.1 released The SCIP Optimization Suite 6.0.1 consists of SCIP 6.0.1, SoPlex 4.0.1, ZIMPL 3.3.7, GCG 3.0.1, and UG 0.8.7. It contains important bugfixes and other improvements for all components of the Optimization Suite, see the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects. |
02/Jul/2018 | SCIP version 6.0.0 released The SCIP Optimization Suite 6.0.0 consists of SCIP 6.0.0, SoPlex 4.0.0, ZIMPL 3.3.6, GCG 3.0.0, and UG 0.8.6. For details regarding the SCIP release, please see the current CHANGELOG. An in-depth description of the new features and improvements of all components of the SCIP Optimization Suite can be found in the technical report The SCIP Optimization Suite 6.0. |
19/Feb/2018 | Visualizing SCIP's branch-and-bound tree Researchers looking for branch-and-bound tree visualizations for SCIP may consider the tool vbc2dot, which has been developed by our colleague Uwe Gotzes. |
older news...
05/Feb/2018 | SCIP version 5.0.1 released This is the first bugfix release for version 5 of the SCIP Optimization Suite. A comprehensive list of the fixes and improvements for SCIP can be found in the release notes and the CHANGELOG. |
21/Dec/2017 | SCIP version 5.0.0 released The SCIP Optimization Suite 5.0.0 consists of SCIP 5.0.0, SoPlex 3.1.0, ZIMPL 3.3.4, GCG 2.1.3, and UG 0.8.5. For more details regarding the SCIP release, please see the current release notes and the CHANGELOG. An in-depth description of the new features and improvements of all components of the SCIP Optimization Suite can be found in the technical report The SCIP Optimization Suite 5.0. |
07/Dec/2017 | We are happy to announce our upcoming SCIP workshop from March 6 to 8, 2018 at RWTH Aachen. The workshop provides a forum for current and prospective SCIP users to discuss their applications and share their experience with SCIP. |
28/Sep/2017 | SCIP featured in the ScaLP library SCIP is interfaced by ScaLP. This new, lightweight C++ wrapper library provides a unique interface to several OR solvers and is developed by the digital technology group at the University of Kassel, Germany. |
01/Sep/2017 | SCIP version 4.0.1 released The SCIP Optimization Suite 4.0.1 consists of SCIP 4.0.1, SoPlex 3.0.1, ZIMPL 3.3.4, GCG 2.1.2, and UG 0.8.4. For more details regarding the SCIP release, please see the current release notes and the CHANGELOG. |
09/Mar/2017 | SCIP version 4.0.0 released The SCIP Optimization Suite 4.0.0 consists of SCIP 4.0.0, SoPlex 3.0.0, ZIMPL 3.3.4, GCG 2.1.2, and UG 0.8.3. For more details regarding the SCIP release, please see the current release notes and the CHANGELOG. An in-depth description of the new features and improvements of all components of the SCIP Optimization Suite can be found in the technical report The SCIP Optimization Suite 4.0. |
01/Sep/2016 | The Java interface is also now available on GitHub: JSCIPOpt. |
08/Jul/2016 | The Python interface has been externalized to GitHub for easier collaboration: PySCIPOpt. We also released a patched Makefile for the SCIP Optimization Suite 3.2.1 necessary to build the updated interface. |
25/May/2016 | Release of Version 2.1.0 of SCIP-SDP, the mixed-integer semidefinite programming plugin for SCIP, developed at TU Darmstadt. |
29/Feb/2016 | SCIP version 3.2.1 released The SCIP Optimization Suite 3.2.1 consists of SCIP 3.2.1, SoPlex 2.2.1, ZIMPL 3.3.3, GCG 2.1.1, and UG 0.8.2. For more details, please see the current CHANGELOG. There is also a technical report about new features and improvements in the SCIP Optimization Suite 3.2. |
27/Oct/2015 | Normaliz in its new release 3.0 uses SCIP for subtasks requiring the solution of Integer Programming problems. Normaliz is a tool for computations in affine monoids, vector configurations, lattice polytopes, and rational cones developed at the University of Osnabrück. |
28/Sep/2015 | Workshop/Lecture/Winter School "Combinatorial Optimization @ Work" is held at ZIB! Check out the program here (including slides of all presentations). |
03/Aug/2015 | DSP – new open-source parallel solver for stochastic mixed-integer programming using SCIP |
31/Jul/2015 | Patched version UG 0.8.1 is released, replacing UG 0.8.0 of the SCIP Optimization Suite 3.2.0. |
01/Jul/2015 | SCIP version 3.2.0 released
(see Release Notes and CHANGELOG). The SCIP Optimization Suite 3.2.0 consists of SCIP 3.2.0, SoPlex 2.2.0, ZIMPL 3.3.3, GCG 2.1.0, and UG 0.8.0. |
30/Jun/2015 | New Release of SCIP-SDP, the mixed integer semidefinite programming plugin for SCIP, developed at TU Darmstadt. |
23/Mar/2015 | Windows binaries and libraries available for download. |
09/Mar/2015 | Upcoming event: Combinatorial Optimization @ Work in Berlin (ZIB) - application deadline: 01/Aug/2015 |
18/Dec/2014 | SCIP version 3.1.1 released The SCIP Optimization Suite 3.1.1 consists of SCIP 3.1.1, SoPlex 2.0.1, ZIMPL 3.3.2, GCG 2.0.1, and UG 0.7.5. See the CHANGELOG for details. |
21/Jul/2014 | OPTI toolbox is now available in version 2.10. OPTimization Interface (OPTI) Toolbox is a free MATLAB toolbox for constructing and solving linear, nonlinear, continuous and discrete optimization problems for Windows users. OPTI Toolbox in its current version comes with SCIP 3.0.2. |
16/Jul/2014 | We are happy to announce our upcoming SCIP workshop from September 30 to October 2, 2014. The workshop provides a forum for current and prospective SCIP users to discuss their applications and share their experience with SCIP. |
16/Mar/2014 | Windows binaries and libraries of SCIP 3.1.0 available for download. |
27/Feb/2014 | SCIP version 3.1.0 released
(see Release Notes and CHANGELOG). The SCIP Optimization Suite 3.1.0 consists of SCIP 3.1.0, SoPlex 2.0.0, ZIMPL 3.3.2, GCG 2.0.0, and UG 0.7.3. |
25/Feb/2014 | Website relaunched. |
16/Oct/2013 | SCIP version 3.0.2 released (bug fix release, see
Release Notes and CHANGELOG). The SCIP Optimization Suite 3.0.2 consists of SCIP 3.0.2, SoPlex 1.7.2, and ZIMPL 3.3.1, GCG 1.1.1, and UG 0.7.2. |
17/Apr/2013 | Released beta-version of SCIP which can solve MIP instances exactly over the rational numbers (based on SCIP 3.0.0). Download the source code and get information here. |
18/Jan/2013 | Recently, Sonja Mars from TU Darmstadt and Lars Schewe from the University of Erlangen-Nürnberg released an SDP-Package for SCIP. |
04/Jan/2013 | SCIP version 3.0.1 released (bug fix release,
see Release Notes and CHANGELOG). The SCIP Optimization Suite 3.0.1 consists of SCIP 3.0.1, SoPlex 1.7.1, and ZIMPL 3.3.1, GCG 1.1.0, and UG 0.7.1. Happy New Year! |
31/Oct/2012 | There are some new interfaces to SCIP available: The OPTI project provides a MATLAB interface; on top of this, YALMIP provides a free modeling language; PICOS is a python interface for conic optimization. Thanks to all developers, in particular Jonathan Currie, Johan Löfberg, and Guillaume Sagnol. |
18/Aug/2012 | The SCIP workshop 2012 will take place at TU Darmstadt on October 8 and 9:
further information See you there! |
01/Aug/2012 | SCIP version 3.0.0 released
(see Release Notes and CHANGELOG). The SCIP Optimization Suite 3.0.0 consists of SCIP 3.0.0, SoPlex 1.7.0, ZIMPL 3.3.0, GCG 1.0.0, and UG 0.7.0. |
28/Dec/2011 | SCIP version 2.1.1 released (bug fix release,
see Release Notes and CHANGELOG). The ZIB Optimization Suite 2.1.1 consists of SCIP 2.1.1, SoPlex 1.6.0, and ZIMPL 3.2.0. |
31/Oct/2011 | SCIP version 2.1.0 released
(see Release Notes and CHANGELOG). The ZIB Optimization Suite 2.1.0 consists of SCIP 2.1.0, SoPlex 1.6.0, and ZIMPL 3.2.0. |
26/Aug/2011 | SCIP version 2.0.2 released (see Release Notes and CHANGELOG). |
04/Jan/2011 | SCIP version 2.0.1 released (see Release Notes). The ZIB Optimization Suite 2.0.1 consists of SCIP 2.0.1, SoPlex 1.5.0, and ZIMPL 3.1.0 |
12/Nov/2010 | There was a performance issue with the precompiled SCIP 2.0.0 binaries for Windows/PC which were compiled with the compilers cl 15 and Intel 11.1. If you downloaded these binaries before 12/Nov/2010, we recommend to download these binaries again. |
30/Sep/2010 | SCIP version 2.0.0 released (see Release Notes). The ZIB Optimization Suite 2.0.0 consists of SCIP 2.0.0, SoPlex 1.5.0, and ZIMPL 3.1.0 |
12/Jan/2010 | A bug in the Makefiles of the SCIP examples may cause data loss. The SCIP 1.2.0 tarball in the download section has been patched. We strongly recommend to replace your current SCIP installation. If you have a custom Makefile, please ensure, that the target "clean" is changed as described here. |
15/Sep/2009 | SCIP version 1.2.0 released (see Release Notes). The ZIB Optimization Suite 1.2.0 consists of SCIP 1.2.0, SoPlex 1.4.2, and ZIMPL 3.0.0 |
13/Sep/2009 | Ryan J. O'Neil provides a SCIP-python interface |
04/Jul/2009 | The results of the Pseudo-Boolean Competition 2009 are online. SCIP-Soplex participated in twelve categories and scored first eight times, second three times. SCIP-Clp participated in nine categories and scored first five times, second two times. Detailed results. |
20/Feb/2009 | SoPlex version 1.4.1 and Clp version 1.9.0 have been released. We recommend to upw-150. Some precompiled binaries can be found at the download page. |
30/Sep/2008 | Version 1.1.0 released. |
27/Feb/2008 | New SCIP Introduction by Cornelius Schwarz, see further documention. |
05/Dec/2007 | Upw-150d LP-interface for Mosek, see the download page. |
11+12/Oct/2007 | SCIP Workshop 2007 (in German). |
27/Aug/2007 | Version 1.0 released. |
21/Aug/2007 | Web site relaunched. |
19/Jul/2007 | Tobias Achterberg finished his PhD thesis, which includes a detailed description of SCIP. You can get it here. |
14/May/2007 | Tobias Achterberg submitted his PhD thesis. The log files for SCIP 0.90f and SCIP 0.90i of the benchmarks conducted in the thesis are available here and here. |
01/Sep/2006 | SCIP Version 0.90 released. |
11/Aug/2006 | Linux binaries linked to CLP 1.03.03 available (contributed by Hans Mittelmann). |
11/Jul/2006 | MS Visual C++ project files for SCIP 0.82 contributed by Martin C. Mueller. |
15/May/2006 | SCIP Version 0.82 released. |
03/Jan/2006 | SCIP Version 0.81 released. |
20/Sep/2005 | SCIP Version 0.80 released. |
A similar technique is used for solving both Integer Programs and Constraint Programs: the problem is successively divided into smaller subproblems (branching) that are solved recursively.
On the other hand, Integer Programming and Constraint Programming have different strengths: Integer Programming uses LP relaxations and cutting planes to provide strong dual bounds, while Constraint Programming can handle arbitrary (non-linear) constraints and uses propagation to tighten domains of variables.
SCIP is a framework for Constraint Integer Programming oriented towards the needs of mathematical programming experts who want to have total control of the solution process and access detailed information down to the guts of the solver. SCIP can also be used as a pure MIP and MINLP solver or as a framework for branch-cut-and-price.
SCIP is implemented as C callable library and provides C++ wrapper classes for user plugins. It can also be used as a standalone program to solve mixed integer linear and nonlinear programs given in various formats such as MPS, LP, flatzinc, CNF, OPB, WBO, PIP, etc. Furthermore, SCIP can directly read ZIMPL models.
There are a number of interfaces to SCIP:
Interface | implementing custom plugins | building and solving static models | modifying and iterated solving | querying solution pool | setting parameters |
---|---|---|---|---|---|
C/C++-API | all | yes | yes | yes | yes |
Python/PySCIPOpt | all | yes | yes | yes | yes |
Julia/SCIP.jl | all | yes | yes | yes | yes |
Matlab | no | yes | no | no | yes |
Java/JSCIPOpt | no | yes | no | yes | yes |
AMPL | no | yes | no | no | yes |
GAMS | no | yes | no | yes | yes |
Rust | yes | yes | yes | yes | yes |
The SCIP Optimization Suite is a toolbox for generating and solving mixed integer nonlinear programs, in particular mixed integer linear programs, and constraint integer programs. It consists of the following parts:
SCIP | mixed integer (linear and nonlinear) programming solver and constraint programming framework |
SoPlex | linear programming solver |
PaPILO | parallel presolve for integer and linear optimization |
ZIMPL | mathematical programming language |
UG | parallel framework for mixed integer (linear and nonlinear) programs |
GCG | generic branch-cut-and-price solver |
The user can easily generate linear, mixed integer and mixed integer quadratically constrained programs with the modeling language ZIMPL. The resulting model can directly be loaded into SCIP and solved. In the solution process SCIP may use SoPlex as underlying LP solver.
Since all six components are available in source code and free for academic use, they are an ideal tool for academic research purposes and for teaching mixed integer programming.
Download the SCIP Optimization Suite below.
A further extension of SCIP in order to solve MISDPs (mixed-integer semidefinite programs) is available from TU Darmstadt: SCIP-SDP.
The SCIP Optimization Suite 9.0
Suresh Bolusani, Mathieu Besançon, Ksenia Bestuzheva, Antonia Chmiela, João Dionísio, Tim Donkiewicz, Jasper van Doornmalen, Leon Eifler, Mohammed Ghannam, Ambros Gleixner, Christoph Graczyk, Katrin Halbig, Ivo Hedtke, Alexander Hoen, Christopher Hojny, Rolf van der Hulst, Dominik Kamp, Thorsten Koch, Kevin Kofler, Jurgen Lentz, Julian Manns, Gioni Mexi, Erik Mühmer, Marc E. Pfetsch, Franziska Schlösser, Felipe Serrano, Yuji Shinano, Mark Turner, Stefan Vigerske, Dieter Weninger, Lixing Xu
Available at Optimization Online and as ZIB-Report 24-02-29, February 2024
BibTeX
Enabling Research through the SCIP Optimization Suite 8.0
Ksenia Bestuzheva, Mathieu Besançon, Wei-Kun Chen, Antonia Chmiela, Tim Donkiewicz, Jasper van Doornmalen, Leon Eifler, Oliver Gaul, Gerald Gamrath, Ambros Gleixner, Leona Gottwald, Christoph Graczyk, Katrin Halbig, Alexander Hoen, Christopher Hojny, Rolf van der Hulst, Thorsten Koch, Marco Lübbecke, Stephen J. Maher, Frederic Matter, Erik Mühmer, Benjamin Müller, Marc E. Pfetsch, Daniel Rehfeldt, Steffan Schlein, Franziska Schlösser, Felipe Serrano, Yuji Shinano, Boro Sofranac, Mark Turner, Stefan Vigerske, Fabian Wegscheider, Philipp Wellner, Dieter Weninger, Jakob Witzig
Available at ACM Digital Library, June 2023
BibTeX
The SCIP Optimization Suite 8.0
Ksenia Bestuzheva, Mathieu Besançon, Wei-Kun Chen, Antonia Chmiela, Tim Donkiewicz, Jasper van Doornmalen, Leon Eifler, Oliver Gaul, Gerald Gamrath, Ambros Gleixner, Leona Gottwald, Christoph Graczyk, Katrin Halbig, Alexander Hoen, Christopher Hojny, Rolf van der Hulst, Thorsten Koch, Marco Lübbecke, Stephen J. Maher, Frederic Matter, Erik Mühmer, Benjamin Müller, Marc E. Pfetsch, Daniel Rehfeldt, Steffan Schlein, Franziska Schlösser, Felipe Serrano, Yuji Shinano, Boro Sofranac, Mark Turner, Stefan Vigerske, Fabian Wegscheider, Philipp Wellner, Dieter Weninger, Jakob Witzig
Available at Optimization Online and as ZIB-Report 21-41, December 2021
BibTeX
The SCIP Optimization Suite 7.0
Gerald Gamrath, Daniel Anderson, Ksenia Bestuzheva, Wei-Kun Chen, Leon Eifler, Maxime Gasse, Patrick Gemander, Ambros Gleixner, Leona Gottwald, Katrin Halbig, Gregor Hendel, Christopher Hojny, Thorsten Koch, Pierre Le Bodic, Stephen J. Maher, Frederic Matter, Matthias Miltenberger, Erik Mühmer, Benjamin Müller, Marc Pfetsch, Franziska Schlösser, Felipe Serrano, Yuji Shinano, Christine Tawfik, Stefan Vigerske, Fabian Wegscheider, Dieter Weninger, Jakob Witzig
Available at Optimization Online and as ZIB-Report 20-10, March 2020
BibTeX
The SCIP Optimization Suite 6.0
Ambros Gleixner, Michael Bastubbe, Leon Eifler, Tristan Gally, Gerald Gamrath, Robert Lion Gottwald, Gregor Hendel, Christopher Hojny, Thorsten Koch, Marco E. Lübbecke, Stephen J. Maher, Matthias Miltenberger, Benjamin Müller, Marc E. 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
Available at Optimization Online and as ZIB-Report 18-26, July 2018
BibTeX
The SCIP Optimization Suite 5.0
Ambros Gleixner, Leon Eifler, Tristan Gally, Gerald Gamrath, Patrick Gemander, Robert Lion Gottwald, Gregor Hendel, Christopher Hojny, Thorsten Koch, Matthias Miltenberger, Benjamin Müller, Marc E. Pfetsch, Christian Puchert, Daniel Rehfeldt, Franziska Schlösser, Felipe Serrano, Yuji Shinano, Jan Merlin Viernickel, Stefan Vigerske, Dieter Weninger, Jonas T. Witt, Jakob Witzig
Available at Optimization Online and as ZIB-Report 17-61, December 2017
BibTeX
The SCIP Optimization Suite 4.0
Stephen J. Maher, Tobias Fischer, Tristan Gally, Gerald Gamrath, Ambros Gleixner, Robert Lion Gottwald, Gregor Hendel, Thorsten Koch, Marco E. Lübbecke, Matthias Miltenberger, Benjamin Müller, Marc E. Pfetsch, Christian Puchert, Daniel Rehfeldt, Sebastian Schenker, Robert Schwarz, Felipe Serrano, Yuji Shinano, Dieter Weninger, Jonas T. Witt, Jakob Witzig
Available at Optimization Online and as ZIB-Report 17-12, March 2017
BibTeX
The SCIP Optimization Suite 3.2
Gerald Gamrath, Tobias Fischer, Tristan Gally, Ambros M. Gleixner, Gregor Hendel, Thorsten Koch, Stephen J. Maher, Matthias Miltenberger, Benjamin Müller, Marc E. Pfetsch, Christian Puchert, Daniel Rehfeldt, Sebastian Schenker, Robert Schwarz, Felipe Serrano, Yuji Shinano, Stefan Vigerske, Dieter Weninger, Michael Winkler, Jonas T. Witt, Jakob Witzig
Available at Optimization Online and as ZIB-Report 15-60, February 2016
BibTeX
In order to reference the general algorithmic design behind constraint integer programming and SCIP's solving techniques regarding mixed-integer linear and nonlinear programming, please cite the following articles:
A more detailed description of SCIP can be found in
The features for the global optimization of convex and nonconvex MINLPs are described in
The extension of SCIP to solve MIPs exactly over rational input data is described in
However, for the latest developments, please consult our series of release reports.
Since November 4 2022, SCIP is licensed under the Apache 2.0 License.
Releases up to and including Version 8.0.2 remain under the ZIB Academic License as indicated by the files contained in the releases. The new license applies from Version 8.0.3 onwards.
The files you can download here come without warranty. Use at your own risk!
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You can either download SCIP alone or the SCIP Optimization Suite (recommended), a complete source code bundle of SCIP, SoPlex, ZIMPL, GCG, PaPILO and UG.
Click here for information on different platforms ...
-O2
.
If problems occur with STL code, you might change to a different implementation by adding
-library=stlport4
to CXX_COMPILER_FLAGS
. (Note: There are different
implementations of the STL on SUN platforms.)
You can also download precompiled executables of SCIP with which you can solve MIP, MINLP, CIP, SAT, or PBO
instances in various formats.
Note that these executables do not include the readline features (i.e., command line editing and history)
due to license issues. However, you can download the free readline wrapper
rlwrap to provide this missing feature to the executables.
If you use conda, you can install the components of the scipoptsuite using the conda packages.
If you know about further projects or papers that use SCIP, please let us know.
For general information or questions about SCIP please write to the SCIP mailing list scip@zib.de after subscribing to it at the SCIP mailing list page.
For licensing questions, please see the license section of the web page and the contact provided there.
Trouble compiling SCIP from source?
Please check the build documentation before sending an email.
For questions about our SCIP interfaces on GitHub please open an issue in the corresponding repository.
The SCIP mailing list can be accessed via the SCIP mailing list page. You can conveniently search the archives using Google: site:listserv.zib.de/pipermail/scip
We are also watching the SCIP tag on stackoverflow.com and will answer your questions there. Note that we will not answer faster only because you posted the same question both to stack overflow and the mailing list.
SCIP has more than 500,000 lines of source code and is definitely not bug free. If you'd like to help us improve SCIP, visit our bug submission page and file a bug report in English or German.
Sebastian Pokutta | Project Head |
Ksenia Bestuzheva | Head of development |
Mathieu Besançon | Mixed-integer linear and non-linear formulations |
Suresh Bolusani | Cutting planes |
Antonia Chmiela | Machine learning for optimization, cutting planes |
Jasper van Doornmalen | Symmetry handling |
Mohammed Ghannam | Rust interface |
Ambros Gleixner | General framework, exact SCIP & SoPlex, verification |
Lara Glessen | Mixed-integer nonlinear programming |
Katrin Halbig | Mixed-integer programming, decomposition heuristics |
Alexander Hoen | Presolving |
Christopher Hojny | Symmetry handling |
Rolf van der Hulst | Network optimization, total unimodularity |
Dominik Kamp | Robustness |
Thorsten Koch | Algebraic Modeling Language ZIMPL |
Marco Lübbecke | Decomposition framework GCG |
Stephen J. Maher | Shared memory parallelization, Benders decomposition |
Gioni Mexi | Primal heuristics, conflict analysis |
Marc Pfetsch | LP solvers, special math programming constraints, symmetry handling |
Yuji Shinano | Parallelization framework UG |
Mark R. Turner | Cutting planes selection and branching |
Stefan Vigerske | Mixed integer nonlinear programming, Test and release management |
Matthias Walter | Multilinear optimization |
Dieter Weninger | Presolving, mixed integer programming, decomposition methods |
Liding Xu | Mixed-integer nonlinear programming |
Tobias Achterberg | Creator and first developer of SCIP |
Timo Berthold | Primal heuristics, branching rules |
Leon Eifler | Verification, exact MIP |
Tobias Fischer | Constraint handler for special ordered sets, type one; cardinality constraint handler |
Tristan Gally | Relaxation Handlers, SCIP-SDP |
Gerald Gamrath | Column generation, mixed integer programming, branching |
Leona Gottwald | Shared memory parallelization, cutting planes, presolving, CMake |
Christoph Graczyk | Machine learning for optimization |
Stefan Heinz | Solution counting, global constraints, conflict analysis |
Gregor Hendel | Primal heuristics, mixed integer programming, solver intelligence, CMake, SCIP documentation |
Julian Manns | Test and release management |
Alexander Martin | Developer of SIP – the predecessor of SCIP |
Matthias Miltenberger | LP interfaces, Python interface, CMake |
Cristina Muñoz | Test management |
Benjamin Müller | Mixed integer nonlinear programming, domain propagation |
Franziska Schlösser | Test and release management |
Felipe Serrano | Nonlinear programming, cutting planes, Python interface |
Boro Šofranac | Parallelization, conflict analysis |
Fabian Wegscheider | Symmetries in mixed integer nonlinear programming |
Michael Winkler | Presolving, pseudo boolean constraint handler |
Jakob Witzig | Reoptimization, conflict analysis, mixed integer programming |
Kati Jarck | Cutting planes, exact integer programming |
Jacob von Holly-Ponientzietz | Presolving |
Matea Miskovic | Primal heuristics |
We are thankful to many people who over the years have contributed code to SCIP, among others:
Daniel Anderson | Treemodel scoring rules, treesize estimation |
Martin Ballerstein | Constraint Handler for bivariate nonlinear constraints |
Chris Beck | Logic-based Bender's decomposition |
Livio Bertacco | Interface to FICO/Xpress |
Andreas Bley | VRP example |
Pierre Le Bodic | Treemodel scoring rules, treesize estimation |
Tobias Buchwald | Dual value heuristic |
Weikun Chen | Dual sparsify presolver |
Frederic Didier | Glop LP interface |
Daniel Espinoza | Interface to QSopt |
John Forrest | Interface to CLP |
Fabian Frickenstein | Verification |
Maxime Gasse | Vanilla full strong branching |
Thorsten Gellermann | Generic NLP interface |
Patrick Gemander | Presolving, mixed integer programming |
Naga Venkata Chaitanya Gudapati | Sudoku example |
Bo Jensen | Interface to MOSEK |
Renke Kuhlmann | Interface to WORHP |
Manuel Kutschka | Separator for {0,1/2}-cuts |
Anna Melchiori | Multi-aggregated variable branching rule |
Dennis Michaels | Constraint Handler for bivariate nonlinear constraints |
Giacomo Nannicini | GMI example |
Michael Perregaard | Interface to FICO/Xpress |
Frédéric Pythoud | Superindicator constraint handler |
Christian Raack | Separator for MCF cuts |
Jörg Rambau | Branch-and-Price contributions |
Daniel Rehfeldt | Steiner Tree Problem application |
Domenico Salvagnin | Feasibility Pump 2.0 |
Sebastian Schenker | PolySCIP |
Jens Schulz | Scheduling plugins: cumulative and linking constraint handler, variable bounds propagator |
Cornelius Schwarz | Queens example |
Robert Schwarz | Python interface |
Felix Hennings | JNI interface |
Yuji Shinano | Parallel extension of SCIP |
Dan Steffy | Exact integer programming |
Timo Strunk | PolySCIP |
Andreas Tuchscherer | Branch-and-Price contributions |
Ingmar Vierhaus | Nonlinear constraint parsing in CIP reader |
Stefan Weltge | OBBT propagator |
© 2024 by Zuse Institute Berlin (ZIB).
For the imprint and privacy statement we refer to the Imprint of ZIB with the following additions and modifications:
The number of SCIP downloads is tracked and used to generate statistics about the downloads and to generate the world map of download locations. The personal information is used to distinguish the number of downloads from the number of users per year that might download more than one version or archive. In addition to the privacy statements of ZIB, we hereby declare that your name and affiliation recorded for the SCIP download is used for purposes of granting licenses and for statistics about software downloads, and is processed and stored on our server for the duration of a year.