cmditch-pyscip-test 0.1.1
Test package
License: MIT
Requires Python: >=3.8

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).

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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.