This project provides an interface from Python to the SCIP Optimization Suite.
Installation
See INSTALL.rst for instructions.
Building and solving a model
There are several examples provided in the tests folder. These display some functionality of the interface and can serve as an entry point for writing more complex code. 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:
It is necessary to import python-scip in your code. This is achieved by including the line
from pyscipopt import ModelCreate a solver instance.
model = Model("Example") # model name is optionalAccess the methods in the scip.pyx 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()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.
Extending the interface
PySCIPOpt already covers many of the SCIP callable library methods. You may also extend it to increase the functionality of this interface. The following will provide some directions on how this can be achieved:
The two most important files in PySCIPOpt are the scip.pxd and scip.pyx. These two files specify the public functions of SCIP that can be accessed from your python code.
To make PySCIPOpt aware of the public functions you would like to access, you must add them to scip.pxd. There are two things that must be done in order to properly add the functions:
Ensure any enums, structs or SCIP variable types are included in scip.pxd
Add the prototype of the public function you wish to access to scip.pxd
After following the previous two steps, it is then possible to create functions in python that reference the SCIP public functions included in scip.pxd. This is achieved by modifying the scip.pyx file to add the functionality you require.
We are always happy to accept pull request containing patches or extensions!
Please have a look at our contribution guidelines.
Gotchas
Ranged constraints
While ranged constraints of the form
lhs <= expression <= rhsare supported, the Python syntax for chained comparisons can’t be hijacked with operator overloading. Instead, parenthesis must be used, e.g.,
lhs <= (expression <= rhs)Alternatively, you may call model.chgRhs(cons, newrhs) or model.chgLhs(cons, newlhs) after the single-sided constraint has been created.
Variable objects
You can’t use Variable objects as elements of sets or as keys of dicts. They are not hashable and comparable. The issue is that comparisons such as x == y will be interpreted as linear constraints, since Variables are also Expr objects.
Dual values
While PySCIPOpt supports access to the dual values of a solution, there are some limitations involved:
Can only be used when presolving and propagation is disabled to ensure that the LP solver - which is providing the dual information - actually solves the unmodified problem.
Heuristics should also be disabled to avoid that the problem is solved before the LP solver is called.
There should be no bound constraints, i.e., constraints with only one variable. This can cause incorrect values as explained in #136
Therefore, you should use the following settings when trying to work with dual information:
model.setPresolve(pyscipopt.SCIP_PARAMSETTING.OFF)
model.setHeuristics(pyscipopt.SCIP_PARAMSETTING.OFF)
model.disablePropagation()1 maintainer
Release files for PySCIPOpt 2.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| PySCIPOpt-2.0.2.tar.gz | 511.0 kB | Details |
Built distributions (wheels)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| PySCIPOpt-2.0.2-cp36-cp36m-win_amd64.whl | CPython 3.6 | CPython 3.6 pymalloc | Windows x86-64 | Details |
| PySCIPOpt-2.0.2-cp35-cp35m-win_amd64.whl | CPython 3.5 | CPython 3.5 pymalloc | Windows x86-64 | Details |
| PySCIPOpt-2.0.2-cp27-cp27m-win_amd64.whl | CPython 2.7 | CPython 2.7 pymalloc | Windows x86-64 | Details |
Total release size: 2.0 MB
Release files / PySCIPOpt-2.0.2.tar.gz
| Download URL | PySCIPOpt-2.0.2.tar.gz |
|---|---|
| Size | 511.0 kB |
| Tags | Source |
|
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Release files / PySCIPOpt-2.0.2-cp36-cp36m-win_amd64.whl
| Download URL | PySCIPOpt-2.0.2-cp36-cp36m-win_amd64.whl |
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| Size | 529.9 kB |
| Tags | CPython 3.6 CPython 3.6 pymalloc Windows x86-64 |
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Release files / PySCIPOpt-2.0.2-cp35-cp35m-win_amd64.whl
| Download URL | PySCIPOpt-2.0.2-cp35-cp35m-win_amd64.whl |
|---|---|
| Size | 488.7 kB |
| Tags | CPython 3.5 CPython 3.5 pymalloc Windows x86-64 |
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Release files / PySCIPOpt-2.0.2-cp27-cp27m-win_amd64.whl
| Download URL | PySCIPOpt-2.0.2-cp27-cp27m-win_amd64.whl |
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| Size | 517.5 kB |
| Tags | CPython 2.7 CPython 2.7 pymalloc Windows x86-64 |
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