PySCIPOpt
=========
This project provides an interface from Python to the [SCIP Optimization Suite](https://www.scipopt.org/). Starting from v8.0.3, SCIP uses the [Apache2.0](https://www.apache.org/licenses/LICENSE-2.0) license. If you plan to use an earlier version of SCIP, please review [SCIP's license restrictions](https://scipopt.org/index.php#license).
[](https://gitter.im/PySCIPOpt/Lobby)
[](https://pypi.python.org/pypi/pyscipopt)
[](https://github.com/scipopt/PySCIPOpt/actions/workflows/integration-test.yml)
[](https://app.codecov.io/gh/scipopt/pyscipopt/)
[](https://ci.appveyor.com/project/mattmilten/pyscipopt/branch/master)
Documentation
-------------
Please consult the [online documentation](https://pyscipopt.readthedocs.io/en/latest/) 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](https://scipopt.github.io/PySCIPOpt/docs/html/index.html)
See [CHANGELOG.md](https://github.com/scipopt/PySCIPOpt/blob/master/CHANGELOG.md) for added, removed or fixed functionality.
Installation
------------
The recommended installation method is via PyPI
```bash
pip install pyscipopt
```
For information on specific versions, installation via Conda, and guides for building from source,
please see the [online documentation](https://pyscipopt.readthedocs.io/en/latest/install.html).
Building and solving a model
----------------------------
There are several [examples](https://github.com/scipopt/PySCIPOpt/blob/master/examples/finished) and
[tutorials](https://github.com/scipopt/PySCIPOpt/blob/master/examples/tutorial). 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](https://github.com/scipopt/PySCIPOpt/blob/master/src/pyscipopt/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
``` {.sourceCode .python}
from pyscipopt import Model
```
2) Create a solver instance.
``` {.sourceCode .python}
model = Model("Example") # model name is optional
```
3) Access the methods in the `scip.pxi` file using the solver/model
instance `model`, e.g.:
``` {.sourceCode .python}
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]))
```
Writing new plugins
-------------------
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.
Citing PySCIPOpt
----------------
Please cite [this paper](https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/6045)
```
@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](https://scip.zib.de/index.php#cite) when you use this tool for a publication or other scientific work.
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"description": "PySCIPOpt\n=========\n\nThis project provides an interface from Python to the [SCIP Optimization Suite](https://www.scipopt.org/). Starting from v8.0.3, SCIP uses the [Apache2.0](https://www.apache.org/licenses/LICENSE-2.0) license. If you plan to use an earlier version of SCIP, please review [SCIP's license restrictions](https://scipopt.org/index.php#license).\n\n[](https://gitter.im/PySCIPOpt/Lobby)\n[](https://pypi.python.org/pypi/pyscipopt)\n[](https://github.com/scipopt/PySCIPOpt/actions/workflows/integration-test.yml)\n[](https://app.codecov.io/gh/scipopt/pyscipopt/)\n[](https://ci.appveyor.com/project/mattmilten/pyscipopt/branch/master)\n\n\nDocumentation\n-------------\n\nPlease consult the [online documentation](https://pyscipopt.readthedocs.io/en/latest/) or use the `help()` function directly in Python or `?` in IPython/Jupyter.\n\nThe old documentation, which we are in the process of migrating from,\nis still more complete w.r.t. the API, and can be found [here](https://scipopt.github.io/PySCIPOpt/docs/html/index.html)\n\nSee [CHANGELOG.md](https://github.com/scipopt/PySCIPOpt/blob/master/CHANGELOG.md) for added, removed or fixed functionality.\n\nInstallation\n------------\n\nThe recommended installation method is via PyPI\n```bash\npip install pyscipopt\n```\n\nFor information on specific versions, installation via Conda, and guides for building from source,\nplease see the [online documentation](https://pyscipopt.readthedocs.io/en/latest/install.html).\n\nBuilding and solving a model\n----------------------------\n\nThere are several [examples](https://github.com/scipopt/PySCIPOpt/blob/master/examples/finished) and\n[tutorials](https://github.com/scipopt/PySCIPOpt/blob/master/examples/tutorial). These display some functionality of the\ninterface and can serve as an entry point for writing more complex code. Some of the common usecases are also available in the [recipes](https://github.com/scipopt/PySCIPOpt/blob/master/src/pyscipopt/recipes) sub-package.\nYou might also want to have a look at this article about PySCIPOpt:\n<https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/6045>. The\nfollowing steps are always required when using the interface:\n\n1) It is necessary to import python-scip in your code. This is achieved\n by including the line\n\n``` {.sourceCode .python}\nfrom pyscipopt import Model\n```\n\n2) Create a solver instance.\n\n``` {.sourceCode .python}\nmodel = Model(\"Example\") # model name is optional\n```\n\n3) Access the methods in the `scip.pxi` file using the solver/model\n instance `model`, e.g.:\n\n``` {.sourceCode .python}\nx = model.addVar(\"x\")\ny = model.addVar(\"y\", vtype=\"INTEGER\")\nmodel.setObjective(x + y)\nmodel.addCons(2*x - y*y >= 0)\nmodel.optimize()\nsol = model.getBestSol()\nprint(\"x: {}\".format(sol[x]))\nprint(\"y: {}\".format(sol[y]))\n```\n\nWriting new plugins\n-------------------\n\nThe Python interface can be used to define custom plugins to extend the\nfunctionality of SCIP. You may write a pricer, heuristic or even\nconstraint handler using pure Python code and SCIP can call their\nmethods using the callback system. Every available plugin has a base\nclass that you need to extend, overwriting the predefined but empty\ncallbacks. Please see `test_pricer.py` and `test_heur.py` for two simple\nexamples.\n\nPlease notice that in most cases one needs to use a `dictionary` to\nspecify the return values needed by SCIP.\n\nCiting PySCIPOpt\n----------------\n\nPlease cite [this paper](https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/6045)\n```\n@incollection{MaherMiltenbergerPedrosoRehfeldtSchwarzSerrano2016,\n author = {Stephen Maher and Matthias Miltenberger and Jo{\\~{a}}o Pedro Pedroso and Daniel Rehfeldt and Robert Schwarz and Felipe Serrano},\n title = {{PySCIPOpt}: Mathematical Programming in Python with the {SCIP} Optimization Suite},\n booktitle = {Mathematical Software {\\textendash} {ICMS} 2016},\n publisher = {Springer International Publishing},\n pages = {301--307},\n year = {2016},\n doi = {10.1007/978-3-319-42432-3_37},\n}\n```\nas well as the corresponding [SCIP Optimization Suite report](https://scip.zib.de/index.php#cite) when you use this tool for a publication or other scientific work.\n",
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