Scippy

SCIP

Solving Constraint Integer Programs

About

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.



By default, SCIP comes with a bouquet of different plugins for solving MIPs and MINLPs.

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 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 programs given in MPS, LP, flatzinc, CNF, OPB, WBO, PIP, or CIP format. Besides that SCIP can directly read ZIMPL models.

An outline of SCIP and its algorithmic approach can be found in

and in

A detailed description of SCIP can be found in

The nonlinear solving features are described in


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
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 five 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 (Linux). Previous releases and versions for different platforms are available here.

Locations of registered SCIP downloads

Features

  • very fast standalone solver for linear programming (LP), mixed integer programming (MIP), and mixed integer nonlinear programming (MINLP)
  • framework for branching, cutting plane separation, pricing, and propagation
  • large C-API, C++ wrapper classes for user plugins
  • interfaces to other applications and programming languages (contained in source code packages or available from github.com/SCIP-interfaces):
  • open LP solver support:
  • highly flexible through many possible user plugins:
    • constraint handlers to implement arbitrary constraints,
    • variable pricers to dynamically create problem variables,
    • domain propagators to apply constraint independent propagations on the variables' domains,
    • separators for cutting planes based on the LP relaxation; benefit from a dynamic cut pool management,
    • relaxators can be included to provide relaxations (e.g., semidefinite relaxations or Lagrangian relaxations) and dual bounds in addition to the LP relaxation, working in parallel or interleaved
    • primal heuristics to search for feasible solutions with specific support for probing and diving,
    • node selectors to guide the search,
    • branching rules to split the problem into subproblems; arbitrarily many children per node can be created, and the different children can be arbitrarily defined,
    • presolvers to simplify the solved problem,
    • file readers to parse different input file formats,
    • event handlers to be informed on specific events, e.g., after a node was solved, a specific variable changes its bounds, or a new primal solution is found,
    • display handlers to create additional columns in the solver's output.
    • dialog handlers to extend the included command shell.
    • conflict analysis can be applied to learn from infeasible subproblems
    • dynamic memory management to reduce the number of operation system calls with automatic memory leakage detection in debug mode

News

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.

older news...

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 at http://code.google.com.
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.

License

SCIP is distributed under the ZIB Academic License. You are allowed to retrieve SCIP for research purposes as a member of a non-commercial and academic institution.
If you want to use SCIP commercially or if you are interested in maintenance and support, please contact us by sending an email to Thorsten Koch.

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 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
ZIB-Report 17-61, Zuse Institute Berlin, 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
ZIB-Report 17-12, Zuse Institute Berlin, 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
ZIB-Report 15-60, Zuse Institute Berlin, February 2016
Bibtex

Download

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

You can either download SCIP alone or the SCIP Optimization Suite (recommended), a complete source code bundle of SCIP, SoPlex, ZIMPL, GCG, and UG.

You can also download precompiled executables of SCIP with which you can solve MIP, MIQCP, CIP, SAT, or PBO instances in MPS, LP, RLP, ZIMPL, flatzinc, CNF, OPB, WBO, PIP, or CIP format.
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.

Here are the MIPLIB 2010 MPS files assembled as an archive: miplib2010-benchmark.tgz.

older versions...

Platforms

SCIP is completely implemented in C. The code should compile with any C compiler that supports the C99 standard.

We have tested SCIP with compilers from

  • GNU
  • Compaq
  • Intel
  • SUN

on 32- and 64-bit versions of

  • Linux
  • Mac
  • Windows
  • SunOS
  • Android

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.

Hints for SUN platforms

  • If you are using the GNU compiler and you experience a strange behavior of your program (segmentation faults), you might try a reduce the optimization level in make.sunos.sparc.gnu.opt by changing -O3 to -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.)

Contact

For general information or questions about SCIP please write to scip@zib.de. 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

We are also watching the SCIP tag on stackoverflow.com and will answer your questions there.

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.

Developers

Current developers

Tristan Gally Relaxation Handlers
Gerald Gamrath Column generation, mixed integer programming, branching
Patrick Gemander Presolving, mixed integer programming
Ambros Gleixner SoPlex interface, mixed integer nonlinear programming, project head
Robert Gottwald Shared memory parallelization, cutting planes, mixed integer programming, CMake
Gregor Hendel Primal heuristics, mixed integer programming, solver intelligence, CMake
Christopher Hojny Symmetry handling
Thorsten Koch Project head
Stephen J. Maher Shared memory parallelization, decomposition
Matthias Miltenberger LP interfaces, Python interface, CMake
Benjamin Müller Mixed integer nonlinear programming, domain propagation
Marc Pfetsch Special math programming constraints, symmetry handling, former project head
Franziska Schlösser Test management
Felipe Serrano Nonlinear programming, cutting planes, Python interface
Jan Merlin Viernickel Student assistant, mixed integer programming
Stefan Vigerske Mixed integer nonlinear programming
Dieter Weninger Presolving, mixed integer programming
Jakob Witzig Reoptimization, conflict analysis, mixed integer programming

Former developers

Tobias Achterberg Former main developer
Timo Berthold Former main developer, primal heuristics, branching rules
Stefan Heinz Former main developer, solution counting, global constraints, conflict analysis
Tobias Fischer Constraint handler for special ordered sets, type one; cardinality constraint handler
Alexander Martin Developer of SIP – the predecessor of SCIP
Michael Winkler Former main developer, presolving, pseudo boolean constraint handler
Kati Wolter Former main developer, cutting planes, exact integer programming

Contributors

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
Tobias Buchwald Dual value heuristic
Leon Eifler Cycle clustering application
Daniel Espinoza Interface to QSopt
John Forrest Interface to CLP
Thorsten Gellermann Generic NLP interface
Bo Jensen Interface to MOSEK
Renke Kuhlmann Interface to WORHP
Manuel Kutschka Separator for {0,1/2}-cuts
Alexandra Kraft Former student assistant
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 Simon 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
Robert Waniek Former student assistant
Stefan Weltge OBBT propagator

Workshop

Every now and then there are SCIP Workshops where developers and users come together to discuss different approaches and implementations. The next workshop will be held in Aachen from March 6 to 8, 2018, including an introduction day dedicated for newcomers.

Click here for further information. Sign up for the SCIP mailing list to get notified about future events.

Related Work

Compiled list of all publications about SCIP on swMath.org

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

Cooperation

SCIP is developed in cooperation with

Solving MIPs exactly over the rational numbers

As all standard MIP solvers, SCIP works with finite precision binary floating-point arithmetic. This allows efficient computations, but also introduces rounding errors. To some extent, rounding errors can be handled by using tolerances at certain parts of the solving process (e.g., when testing the feasibility of primal solutions w.r.t. constraints and bounds). However, this by no means guarantees truely correct results.

For most applications, the errors can be neglected. This situation changes fundamentally, if MIPs are used to study theoretical problems, if feasibility questions are considered, and if wrong answers can have legal consequences. For such applications, an exact MIP solver is required.

There is now a beta-version of SCIP available (based on SCIP 3.0.0) which can solve MIP instances exactly over the rational numbers. So far, the branch-and-bound algorithm of SCIP has been adapted for exact MIP solving. It works with safe primal and dual bounds and supports all branching plugins of SCIP. All additional plugins, like separators, primal heuristics, and presolvers must not be included (adding them might introduce rounding errors).

This project is supported by the DFG Priority Program 1307 "Algorithm Engineering".


Authors

Notes

  • This is a beta-version.
  • The code only supports solving MIPs.
  • The code can only be used as a black box solver.

Download

You can download the source code of the SCIP distribution that supports solving MIPs exactly here.

It can be compiled in exact solving mode or in standard solving mode using floating-point arithmetic. In the latter case, you will get the same solver/behavior as with the official distribution of SCIP 3.0.0.

For information, see "Installation description for SCIP in exact solving mode" in the contained INSTALL file and "How to use SCIP in exact solving mode" in your local doxygen docu (via "make doc").

References