Welcome!

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.

News

Jan/2025	SCIP version 9.2.1 The SCIP Optimization Suite 9.2.1 consists of SCIP 9.2.1, GCG 3.7.1, SoPlex 7.1.3, ZIMPL 3.6.2, PaPILO 2.4.1 and UG 1.0.0 beta 6. Please check the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects.
Nov/2024	SCIP version 9.2.0 The SCIP Optimization Suite 9.2.0 consists of SCIP 9.2.0, GCG 3.7.0, SoPlex 7.1.2, ZIMPL 3.6.2, PaPILO 2.4.0 and UG 1.0.0 beta 6. Please check the CHANGELOG of SCIP or browse the individual CHANGELOGs of the other projects.
Sep/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.
Jun/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.

older news...

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.
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.

About

What is SCIP?

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.

Detailed list of SCIP's features

What is the SCIP Optimization Suite?

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.

License

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.

Download

The files you can download here come without warranty. Use at your own risk!

Version 9.2.19.2.09.1.19.1.09.0.19.0.08.1.08.0.48.0.38.0.28.0.18.0.07.0.37.0.27.0.17.0.06.0.26.0.16.0.05.0.15.0.04.0.14.0.03.2.13.2.03.1.13.1.03.0.23.0.13.0.02.1.12.1.02.0.22.0.12.0.01.2.01.1.00.0.0any

OS Linux Windows MacOS Raspberry

Source Code

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 ...

Precompiled Packages

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.

Conda

If you use conda, you can install the components of the scipoptsuite using the conda packages.

Notes

• Symmetry handling limits the number of search nodes and generators during the backtrack search and improves performance of symmetry handling in SCIP. Since SCIP version 9.1 nauty is the default symmetry library with sassy as symmetry presolving library. Nauty as well as sassy are included in the SCIP repository and are find automatically. An alternative to nauty is bliss. A patched bliss fork is available on GitHub.
• Windows Defender may block the installation of the SCIP Optimization Suite installer. Try adding an exclusion to Windows Security to work around this.
• For some of the mac packages you need the libgfortran and libtbb libraries, and bison. You can install them for example via homebrew: `brew install bison`, `brew install gcc` and `brew install tbb` before installing the scipoptsuite.
  After installation of the .sh package to a custom directory you may need to include the lib folder into your DYLD_LIBRARY_PATH.
  If you want to compile the Suite on a apple silicon M series processor you might need to set `ARCH=arm` as an argument to make.
• The linux precompiled binaries are built on debian and ubuntu, both debian based distributions. Chances are that you won't be able to install them on a different one, like arch-linux.
• If you download an executable containing PaPILO you might need a working installation of the TBB library in your machine. (This might also be available via your package manager, for debian, for example, it is called `libtbb2`.)
• For GCG on a linux system you need a working installation of hMETIS version >= 2.0, and zsh (apt install zsh) in your system.
• Here is the download section of MIPLIB 2017 benchmark MPS files.
• SCIP is also available on the NEOS Server, where you can post your model in LP or MPS format, or as an AMPL, GAMS, or ZIMPL model and let the NEOS Server solve it with SCIP linked to CPLEX.
• A development version of SCIP 7 to solve MIPs exactly without floating-point roundoff errors is available as a separate download on Github .

Interfaces and LP Solvers usable with SCIP

There are many interfaces to SCIP. The following are included in SCIP or available at https://github.com/scipopt:

Interface	implementing custom plugins	building and solving static models	modifying and iterated solving	querying solution pool	setting parameters
C/C++-API	yes	yes	yes	yes	yes
Python/PySCIPOpt	yes	yes	yes	yes	yes
Julia/SCIP.jl	yes	yes	yes	yes	yes
Rust/Russcip	yes	yes	yes	yes	yes
MatlabSCIPInterface	no	yes	no	no	yes
Java/JSCIPOpt	no	yes	no	yes	yes
C++/SCIP++	no	yes	no	no	yes
AMPL	no	yes	no	no	yes

For further details, check out the interface section of the documentation.

Also, a number of LP solvers can be linked and used by SCIP:

• CLP
• CPLEX
• FICO XPRESS
• Glop
• Gurobi
• HiGHS (interface in beta status)
• Mosek
• QSopt
• SoPlex

How to Cite

Any publication for which SCIP or the SCIP Optimization Suite is used must include an acknowledgement and a reference to one of the following articles, depending on the version used:

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

Click here for a list of previous release reports...

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:

• Constraint Integer Programming: a New Approach to Integrate CP and MIP
  Tobias Achterberg, Timo Berthold, Thorsten Koch, Kati Wolter
  Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2008, LNCS 5015, Pages 6–20, 2008
  Available as ZIB Report 08-01 and at SpringerLink, January 2008
  BibTeX

and

• SCIP: Solving Constraint Integer Programs
  Tobias Achterberg
  Mathematical Programming Computation, Volume 1, Number 1, Pages 1–41, 2009.
  Available at Mathematical Programming Computation, 2009
  BibTeX

A more detailed description of SCIP can be found in

• Constraint Integer Programming
  Tobias Achterberg
  Ph.D. thesis, TU Berlin, July 2007.
  Available as Doctoral Thesis, January 2009
  BibTeX

The features for the global optimization of convex and nonconvex MINLPs are described in

• Global Optimization of Mixed-Integer Nonlinear Programs with SCIP 8
  Ksenia Bestuzheva, Antonia Chmiela, Benjamin Müller, Felipe Serrano, Stefan Vigerske, Fabian Wegscheider
  arXiv:2301.00587, 2023.
  BibTeX

The extension of SCIP to solve MIPs exactly over rational input data is described in

• A Computational Status Update for Exact Rational Mixed Integer Programming
  Leon Eifler, Ambros Gleixner
  Integer Programming and Combinatorial Optimization. IPCO 2021, Lecture Notes in Computer Science, vol 12707. pp. 163-177
  BibTeX

However, for the latest developments, please consult our series of release reports.

Contact

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.

Mailing List

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

Stack Overflow

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Reporting Bugs

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.

Team Members

Current

Ksenia Bestuzheva	Head of development, Mixed-integer nonlinear programming
Suresh Bolusani	Cutting planes
Antonia Chmiela	Machine learning for optimization, cutting planes
João Dionísio	Python interface
Tim Donkiewicz	Decomposition framework GCG
Leon Eifler	Verification, exact SCIP & SoPlex
Oliver Gaul	Decomposition framework GCG
Mohammed Ghannam	Rust interface, Python interface
Ambros Gleixner	General framework, exact SCIP & SoPlex, verification
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	Benders decomposition
Gioni Mexi	Conflict analysis, Primal heuristics, Pseudoboolean optimization, Branching
Erik Mühmer	Decomposition framework GCG
Marc Pfetsch	General framework, LP solvers, special constraints, symmetry handling
Sebastian Pokutta	Project Head
Yuji Shinano	Parallelization framework UG
Mark R. Turner	Cutting planes selection, branching, Python interface
Stefan Vigerske	Mixed-integer nonlinear programming, Release management, Compiler whisperer
Matthias Walter	Multilinear optimization, CMake build system
Dieter Weninger	Presolving, mixed integer programming, decomposition methods
Liding Xu	Mixed-integer nonlinear programming

Former

Tobias Achterberg	Creator and first developer of SCIP
Timo Berthold	Primal heuristics, branching rules
Mathieu Besançon	Mixed-integer linear and non-linear formulations
Jasper van Doornmalen	Symmetry handling
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
Katrin Halbig	Mixed-integer programming, decomposition heuristics
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

Student Assistants

Jacob von Holly-Ponientzietz	Presolving
Matea Miskovic	Primal heuristics

Click here for a list of further contributors ...

Cooperation

SCIP is developed in cooperation with

We appreciate the support of

Related Work and Users

An up-to-date list of publications about SCIP can be found at swMath.org.

Projects at ZIB that use SCIP (incomplete)

• Optimization of Gas Transport,DFG Research Center Matheon, Project B20.
• Advanced Solver Technology for SCM
• DESI – Durchgängig energiesensible IKT-Produktion
• Exact Integer Programming, DFG Priority Program 1307 Algorithm Engineering.
• Integrated Planning of Multi-layer Networks, DFG Research Center Matheon,  Project B3.
• Service Design in Public Transport, DFG Research Center Matheon,  Project B15.
• ForNe: Research Cooperation Network Optimization
• Chip Design Verification, DFG Research Center Matheon,  Project D17.
• Discrete Morse Functions.
• Infeasible Linear Inequality Systems.
• MLTN - Multi-layer Transport Networks
• Stable sets and special graph classes
• Symmetries in Integer Programming, DFG Research Center Matheon,  Project B12.
• VeriCount - Counting Solutions in the Field of Verification
• Scheduling the SBB Cargo Railroad routing and shipment operations at night, Combinatorial Optimization & Graph Algorithms Group, TU Berlin.
• Scheduling Techniques in Constraint Integer Programming, DFG Research Center Matheon,  Project B25.

Projects using SCIP (outside ZIB, incomplete)

• Project A9: Resilient Design from SFB 805: Control of Uncertainties in Load-Carrying Structures in Mechanical Engineering, TU Darmstadt
• Projekt EXPRESS: Exploiting Structure in Compressed Sensing Using Side Constraints from SPP 1798: Compressed Sensing in Information Processing (CoSIP), TU Darmstadt
• Project A01: Global Methods for Stationary Gastransport from Transregio/SFB 154: Mathematical Modelling, Simulation and Optimization on the Example of Gas Networks, TU Darmstadt
• Project A4: Mathematical Models and Methods for an Optimum Combination of Passive and Active Components from SFB 805: Control of Uncertainties in Load-Carrying Structures in Mechanical Engineering, TU Darmstadt
• DSP – Parallel Solver for Stochastic Mixed-integer Programming Problems, Argonne National Laboratory
• GOBNILP – Globally Optimal Bayesian Network learning using Integer Linear Programming, The University of York
• SCIL – Symbolic Constraints in Integer Linear programming, Max-Planck-Institut für Informatik
• Generic Column Generation , RWTH Aachen
• Normaliz, a tool for computations in affine monoids, vector configurations, lattice polytopes, and rational cones developed at the University of Osnabrück.
• Numberjack – A python constraint programming platform, University College Cork
• Thermos
  Map-driven web-based software for optimising the layout of heat networks, and associated applications, Centre for Sustainable Energy, UK
  

Some Papers that use SCIP

• Conflict Analysis in Mixed Integer Programming
  Tobias Achterberg
  Discrete Optimization, Special Issue 4, 2007
• Hybrid Branching
  Tobias Achterberg, Timo Berthold
  Proc. of CPAIOR 2009, LNCS 5547, 05.2009.
  
• Improving the Feasibility Pump
  Tobias Achterberg, Timo Berthold
  Discrete Optimization, Special Issue 4, 2007
• Constraint Integer Programming: Techniques and Applications
  Tobias Achterberg, Timo Berthold, Stefan Heinz, Thorsten Koch, Kati Wolter
  ZIB-Report 08-43.
  
• Constraint Integer Programming: a New Approach to Integrate CP and MIP
  Tobias Achterberg, Timo Berthold, Thorsten Koch, Kati Wolter
  Proc. of CPAIOR 2008, LNCS 5015, 2008
• Teaching MIP Modeling and Solving
  Tobias Achterberg, Thorsten Koch, and Martin Grötschel
  ORMS Today 33, no. 6
  
• Counting solutions of integer programs using unrestricted subtree detection
  Tobias Achterberg, Stefan Heinz, Thorsten Koch
  Proc. of CPAIOR 2008, LNCS 5015, 2008
• Experiments with Linear and Semidefinite Relaxations for Solving the Minimum Graph Bisection Problem
  Michael Armbruster, Marzena Fügenschuh, Christoph Helmberg, and Alexander Martin
  Technical Report, TU Darmstadt, 2006
  
• Constrained Clustering using Column Generation
  Babaki, B., Guns, T., Nijssen, S.
  CPAIOR, May 2014, Cork, Ireland
  
• Heuristics of the Branch-Cut-and-Price-Framework SCIP
  Timo Berthold
  Operations Research Proceedings 2007
  
• RENS - Relaxation Enforced Neighborhood Search
  Timo Berthold
  ZIB-Report 07-28
  
• Rapid learning for binary programs
  Timo Berthold, Thibaut Feydy, Peter J. Stuckey
  Proc. of CPAIOR 2010, LNCS 6140
• SCIP Optimization Suite を利用した 混合整数(線形/非線形) 計画問題の解法
  Timo Berthold, Ambros Gleixner, Stefan Heinz, Thorsten Koch, and Yuji Shinano.
  ZIB-Report 12-24
  
• Undercover – a primal heuristic for MINLP based on sub-MIPs generated by set covering
  Timo Berthold, Ambros M. Gleixner
  ZIB-Report 09-40
  
• A Constraint Integer Programming Approach for Resource-Constrained Project Scheduling
  Timo Berthold, Stefan Heinz, Marco Lübbecke, Rolf H. Möhring, Jens Schulz
  Proc. of CPAIOR 2010, LNCS 6140
• Nonlinear pseudo-Boolean optimization: relaxation or propagation?
  Timo Berthold, Stefan Heinz, Marc E. Pfetsch
  Theory and Applications of Satisfiability Testing – SAT 2009, LNCS 5584, 2009
  
• Solving Pseudo-Boolean Problems with SCIP
  Timo Berthold, Stefan Heinz, Marc E. Pfetsch
  ZIB-Report 08-12
  
• Extending a CIP framework to solve MIQCPs
  Timo Berthold, Stefan Heinz, Stefan Vigerske
  ZIB-Report 09-23
  
• Comparing MIQCP solvers to a specialised algorithm for mine production scheduling
  Andreas Bley, Ambros M. Gleixner, Thorsten Koch, Stefan Vigerske
  ZIB-Report 09-32
  
• Auslegung heterogener Kommunikationsnetze nach Performance und Wirtschaftlichkeit
  Andreas Bley, Friederich Kupzog, and Adrian Zymolka
  Proc. of the 11th Kasseler Symposium Energie-Systemtechnik: Energie und Kommunikation, 2006
  
• Angebotsplanung im öffentlichen Nahverkehr
  Ralf Borndörfer, Marika Neumann, Marc E. Pfetsch
  ZIB-Report 08-04, to appear in Heureka'08
  
• The Location-Dispatching Problem: polyhedral results and Content Delivery Network Design
  Philippe Chrétienne, Pierre Fouilhoux, Eric Gourdin, and Jean-Mathieu Segura
  Electronic Notes in Discrete Mathematics 26, 867-874, 2010
  
• Coordination of Cluster Ensembles via Exact Methods
  Ioannis T. Christou
  IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(2):279—293, 2011
  
• A Branch-and-Price Algorithm for Multi-mode Resource Leveling
  Eamonn T. Coughlan, Marco E. Lübbecke and Jens Schulz
  Proc. of SEA 2010, LNCS 6049, 226-238, 2010
  
• Models and Algorithms for Maximum Flow Problems Having Semicontinuous Path Flow Constraints
  Robert Curry and J. Cole Smith
  IISE Transactions, 2017
  
• Optimal control of spatial-dynamic processes: the case of biological invasions
  Rebecca S. Epanchin-Niell and James E. Wilen
  Agricultural and Applied Economics Association 2010 Annual Meeting
  
• Integer linear programming models for topology optimization in sheet metal design
  Armin Fügenschuh and Marzena Fügenschuh
  Mathematical Methods of Operations Research, to appear (2008)
  
• Scenario Technique with Integer Programming for Sustainability in Manufacturing
  Armin Fügenschuh, Pia Gausemeier, Günther Seliger, and Semih Severengiz
  Lecture Notes in Business Information Processing 46, 320-331, 2010
  
• Experiments with a Generic Dantzig-Wolfe Decomposition for Integer Programs
  Gerald Gamrath and Marco E. Lübbecke
  Proc. of SEA 2010, LNCS 6049, 239-252, 2010
  
• Ein neuer Ansatz zur Optimierung des Bilanzausgleichs in einem Gasmarktgebiet
  Uwe Gotzes
  Zeitschrift für Energiewirtschaft, 1-13, 2019
  
• Using Model Counting to Find Optimal Distinguishing Tests
  Stefan Heinz and Martin Sachenbacher
  Proc. of CPAIOR 2009, LNCS 5547
  
• Exact and Approximate Sparse Solutions of Underdetermined Linear Equations
  Sadegh Jokar, Marc E. Pfetsch
  ZIB-Report 07-05
  
• Computing Optimal Morse Matchings
  Michael Joswig and Marc E. Pfetsch
  SIAM Journal on Discrete Mathematics 20, no. 1, 2006
  
• Orbitopal Fixing
  Volker Kaibel, Matthias Peinhardt, and Marc E. Pfetsch
  Proc. of the 12th Integer Programming and Combinatorial Optimization conference (IPCO)
  M. Fischetti and D. Williamson (eds.), LNCS 4513, Springer-Verlag, 74-88, 2007
  
• On connectivity limits in ad hoc networks with beamforming antennas
  Moritz Kiese, Christian Hartmann, Julian Lamberty, and Robert Vilzmann
  EURASIP Journal on Wireless Communications and Networking 2009, No.7, 74-88, 02.2009
  
• Approximated segmentation considering technical and dosimetric constraints in intensity-modulated radiation therapy with electrons
  Antje Kiesel and Tobias Gauer
  ArXiv e-prints, May 2010
• Rapid Mathematical Programming or How to Solve Sudoku Puzzles in a few Seconds
  Thorsten Koch
  Operations Research Proceedings 2005
  
• Algorithms to separate {0,1/2}-Chvatal-Gomory cuts
  Arie M. C. A. Koster, Adrian Zymolka and Manuel Kutschka
  Algorithmica 55, No. 2, 375-391
  
• A formulation space search heuristic for packing unequal circles in a fixed size circular container
  C. O. López and J. E. Beasley
  European Journal of Operational Research 251 (2016) 64–73
  
• Large-scale identification of genetic design strategies using local search
  Desmond S. Lun, Graham Rockwell, Nicholas J. Guido, Michael Baym, Jonathan A. Kelner, Bonnie Berger, James E. Galagan, and George M. Church
  Molecular Systems Biology 5, No. 296, 2009
  
• Optimization Methods For Selecting Founder Individuals For Captive Breeding or reintroduction of Endangered Species
  Webb Miller, Stephen J. Wright, Yu Zhang, Stephan C. Schuster, Vanessa M. Hayes
  Pacific Symposium on Biocomputing, 43-53, 2010
  
• Two-layer Network Design by Branch-and-Cut featuring MIP-based Heuristics
  Sebastian Orlowski, Arie M. C. A. Koster, Christian Raack, and Roland Wessäly
  Proceedings of the Third International Network Optimization Conference, 2007
  
• Branch-And-Cut for the Maximum Feasible Subsystem Problem
  Marc E. Pfetsch
  SIAM Journal on Optimization 19, No. 1, 21-38 (2008)
  
• Integer Programming and Sports Rankings
  Christian Raack, Annie Raymond, Thomas Schlechte, Axel Werner
  ZIB-Report 13-19
  
• Rostering from staffing levels: a branch-and-price approach
  Egbert van der Veen and Bart Veltman
  Proc. of the 35th International Conference on Operational Research Applied to Health Services, 1-10, 2009
  
• Faster MIP solutions via new node selection rules
  Daniel T. Wojtaszeka and John W. Chinneck
  Computers & Operations Research 37, 1544-1556, September 2009
  
• Optimal Packings of Congruent Circles on a Square Flat Torus as Mixed-Integer Nonlinear Optimization Problem.
  Vladimir Voloshinov and Sergey Smirnov
  Voevodin V., Sobolev S. (eds) Supercomputing. RuSCDays 2019. Communications in Computer and Information Science, vol 1129. Springer, December 2019
  
• Robust Aircraft Routing
  Chiwei Yan and Jerry Kung
  INFORMS Transportation Science, July 2016
  

If you know about further projects or papers that use SCIP, please let us know.

SCIP is used by academic and industrial partners all around the world, including

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© 2025 by Zuse Institute Berlin (ZIB). For the imprint and privacy statement we refer to the Imprint of ZIB with the following additions and modifications:

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