• very fast standalone solver for mixed integer programming (MIP) and mixed integer nonlinear programming (MINLP)
• framework for branching, cutting plane separation, propagation, pricing, and Benders' decomposition,
• 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
  • plugins to apply Benders' decomposition and implement Benders' cuts,
  • 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
• an exact mode for numerically exact solving.

• If you want to use SCIP via Python, use the PyPI package of PySCIPOpt or Conda.
• If SCIP should be used as a stand-alone solver via the command line or as a library, then, from version 10.0.0 on, you can use the .deb packages for Ubuntu and Debian, the tarballs (.tgz) for most Linux distributions (anything with GLIBC 2.28 or higher) and macOS, and the installers or zip-archives (.exe, .zip) for Windows. Linux packages are available for x86_64/amd64 and aarch64 architecture, macOS packages only for arm64 (Apple Silicon), and Windows packages only for x64 (Intel/AMD CPUs) at the moment. All packages include binaries as well as libraries of the SCIP Optimization Suite (see details for each package).
• If you want to change used external software like the LP-solver or need SCIP in debug mode, you need to download the source code and follow the installation instructions. Only for Windows, also a prebuilt version of SCIP in debug mode is made available.
• If you want to develop code with SCIP, we can use any method. But because it is advisable to develop code in debug mode, you should download and install the source code.

PySCIPOpt

For the Python interface to SCIP, PySCIPOpt, we suggest installing the PyPI package via

pip install pyscipopt

Precompiled Packages

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

Notes

• Windows Defender may block the installation of the SCIP Optimization Suite installer. Try adding an exclusion to Windows Security to work around this.
• The builds do not include the readline features (i.e., command line editing and history) due to restrictions of the GNU license. However, one can download a free readline wrapper rlwrap to provide this missing feature to the executables.

Source Code

Either SCIP alone or the SCIP Optimization Suite (recommended), a complete source code bundle of SCIP, SoPlex, ZIMPL, GCG, PaPILO and UG, can be downloaded. For compilation instructions please consult the installations section of the online documentation.

Other Sources for Precompiled Packages

When using conda, install components of the SCIP Optimization Suite, PySCIPOpt, or PyGCGOpt via

conda install conda-forge::pyscipopt
conda install conda-forge::pygcgopt
conda install conda-forge::gcg
conda install conda-forge::papilo
conda install conda-forge::scip
conda install conda-forge::soplex
conda install conda-forge::zimpl

When using HomeBrew on macOS, install PaPILO, SoPlex or SCIP via

brew install papilo
brew install scip
brew install soplex

SCIP packages built by Julia's Yggdrasil are attached to the SCIP_PaPILO_jll.jl releases.

When using Nix, install packages of the SCIP Optimization Suite via

nix-shell -p scipopt-gcg
nix-shell -p scipopt-papilo
nix-shell -p scipopt-scip
nix-shell -p scipopt-soplex
nix-shell -p scipopt-ug
nix-shell -p scipopt-zimpl

License

Since version 8.0.3, 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.

Due to the inclusion of third-party code, and the possibility to link against various dependencies, a build of SCIP or the SCIP Optimization Suite can touch additional licenses.

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:

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

NEOS server

SCIP is also available on the NEOS Server, where you can post your model in LP, MPS, NL, OSiL format, or as an AMPL, GAMS, or ZIMPL model and let the NEOS Server solve it with SCIP linked to CPLEX.

How to Cite

The SCIP Optimization Suite 10.0
Christopher Hojny, Mathieu Besançon, Ksenia Bestuzheva, Sander Borst, Antonia Chmiela, João Dionísio, Leon Eifler, Mohammed Ghannam, Ambros Gleixner, Adrian Göß, Alexander Hoen, Rolf van der Hulst, Dominik Kamp, Thorsten Koch, Kevin Kofler, Jurgen Lentz, Stephen J. Maher, Gioni Mexi, Erik Mühmer, Marc E. Pfetsch, Sebastian Pokutta, Felipe Serrano, Yuji Shinano, Mark Turner, Stefan Vigerske, Matthias Walter, Dieter Weninger, Liding Xu
Available at Optimization Online, November 2025
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

• 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

Mailing List

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.
You can conveniently search the archives using Google: site:listserv.zib.de/pipermail/scip.
In case of trouble compiling SCIP from source, please check the build documentation before sending an email.

Bug Reports

Bug reports can be submitted by creating issues for the corresponding project on GitHub.
Alternatively, an e-mail can be send to gitlabgit+integer-scipoptsuite-support-3311-issue-@zib.de.

Stack Overflow

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.

Contact Persons

The SCIP Optimization Suite ecosystem is developed in a cooperation of several research groups and industry partners.
Primary contact is Gioni Mexi (ZIB). Contact persons for other groups are Ambros Gleixner, Christopher Hojny, Marco Lübbecke, Marc Pfetch, and Matthias Walter.

Team Members

Current Developers

Former Developers

Click here for a list of further contributors ...

Cooperation

SCIP is developed in cooperation with

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

Imprint and privacy statement

© 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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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.