This project provides an interface from Python to the SCIP Optimization Suite. Starting from v8.0.3, SCIP uses the Apache2.0 license. If you plan to use an earlier version of SCIP, please review SCIP's license restrictions.

Please consult the online documentation or use the help() function directly in Python or ? in IPython/Jupyter.

The old documentation, which we are in the process of migrating from, is still more complete w.r.t. the API, and can be found here

See CHANGELOG.md for added, removed or fixed functionality.

The recommended installation method is via PyPI:

pip install pyscipopt

To avoid interfering with system packages, it's best to use a virtual environment:

python3 -m venv venv     # creates a virtual environment called venv
source venv/bin/activate # activates the environment. On Windows use: venv\Scripts\activate
pip install pyscipopt

Remember to activate the environment (source venv/bin/activate or equivalent) in each terminal session where you use PySCIPOpt. Note that some configurations require the use of virtual environments.

For information on specific versions, installation via Conda, and guides for building from source, please see the online documentation.

There are several examples and tutorials. These display some functionality of the interface and can serve as an entry point for writing more complex code. Some of the common usecases are also available in the recipes sub-package. You might also want to have a look at this article about PySCIPOpt: https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/6045. The following steps are always required when using the interface:

1. It is necessary to import python-scip in your code. This is achieved by including the line

from pyscipopt import Model

2. Create a solver instance.

model = Model("Example")  # model name is optional

3. Access the methods in the scip.pxi file using the solver/model instance model, e.g.:

x = model.addVar("x")
y = model.addVar("y", vtype="INTEGER")
model.setObjective(x + y)
model.addCons(2*x - y*y >= 0)
model.optimize()
sol = model.getBestSol()
print("x: {}".format(sol[x]))
print("y: {}".format(sol[y]))

The Python interface can be used to define custom plugins to extend the functionality of SCIP. You may write a pricer, heuristic or even constraint handler using pure Python code and SCIP can call their methods using the callback system. Every available plugin has a base class that you need to extend, overwriting the predefined but empty callbacks. Please see test_pricer.py and test_heur.py for two simple examples.

Please notice that in most cases one needs to use a dictionary to specify the return values needed by SCIP.

If your project or company is using PySCIPOpt, consider letting us know at scip@zib.de. We are always interested in knowing how PySCIPOpt is being used, and, given permission, would also appreciate adding your company's logo to our website.

If you are creating models with some degree of complexity which don't take too long to solve, also consider sharing them with us. We might want to add them to tests/helpers/utils.py to help make our tests more robust, or add them to our examples.

Please cite this paper

@incollection{MaherMiltenbergerPedrosoRehfeldtSchwarzSerrano2016,
  author = {Stephen Maher and Matthias Miltenberger and Jo{\~{a}}o Pedro Pedroso and Daniel Rehfeldt and Robert Schwarz and Felipe Serrano},
  title = {{PySCIPOpt}: Mathematical Programming in Python with the {SCIP} Optimization Suite},
  booktitle = {Mathematical Software {\textendash} {ICMS} 2016},
  publisher = {Springer International Publishing},
  pages = {301--307},
  year = {2016},
  doi = {10.1007/978-3-319-42432-3_37},
}

as well as the corresponding SCIP Optimization Suite report when you use this tool for a publication or other scientific work.