swiglpk
=======
*Plain python bindings for the GNU Linear Programming Kit (GLPK)*
|PyPI| |License| |Build Status|
Why?
~~~~
*swiglpk* is not a high-level wrapper for GLPK (take a look at
`optlang <https://github.com/biosustain/optlang>`__ if you are
interested in a python-based mathematical programming language). It just
provides plain vanilla `swig <http://www.swig.org/>`__ bindings to the
underlying C library. In constrast to other GLPK wrappers for python
(e.g. `PyGLPK <http://tfinley.net/software/pyglpk/>`__,
`Python-GLPK <http://www.dcc.fc.up.pt/~jpp/code/python-glpk/>`__,
`ctypes-glpk <https://code.google.com/p/ctypes-glpk/>`__,
`ecyglpki <https://github.com/equaeghe/ecyglpki>`__ etc.) it is fairly
version agnostic: it will try to guess the location of the glpk.h header
file (using ``which glpsol``) and then compile the extension for your
particular GLPK installation. Furthermore, swiglpk provides binary wheels
for all major platforms, which are always up-to-date with the most
recent GLPK version (swiglpk versions follow GLPK versioning in the major
and minor version digits to emphasize that).
Please show us some love by staring this repo if you find swiglpk useful!
Installation
~~~~~~~~~~~~
::
pip install swiglpk
That's it. swiglpk comes with binary wheels for Windows, Mac, and Linux. No installation of third-party dependencies necessary.
Example
~~~~~~~
Running the following (slightly adapted) example from the `GLPK
manual <http://kam.mff.cuni.cz/~elias/glpk.pdf>`__ ...
::
from swiglpk import *
ia = intArray(1+1000); ja = intArray(1+1000);
ar = doubleArray(1+1000);
lp = glp_create_prob();
glp_set_prob_name(lp, "sample");
glp_set_obj_dir(lp, GLP_MAX);
glp_add_rows(lp, 3);
glp_set_row_name(lp, 1, "p");
glp_set_row_bnds(lp, 1, GLP_UP, 0.0, 100.0);
glp_set_row_name(lp, 2, "q");
glp_set_row_bnds(lp, 2, GLP_UP, 0.0, 600.0);
glp_set_row_name(lp, 3, "r");
glp_set_row_bnds(lp, 3, GLP_UP, 0.0, 300.0);
glp_add_cols(lp, 3);
glp_set_col_name(lp, 1, "x1");
glp_set_col_bnds(lp, 1, GLP_LO, 0.0, 0.0);
glp_set_obj_coef(lp, 1, 10.0);
glp_set_col_name(lp, 2, "x2");
glp_set_col_bnds(lp, 2, GLP_LO, 0.0, 0.0);
glp_set_obj_coef(lp, 2, 6.0);
glp_set_col_name(lp, 3, "x3");
glp_set_col_bnds(lp, 3, GLP_LO, 0.0, 0.0);
glp_set_obj_coef(lp, 3, 4.0);
ia[1] = 1; ja[1] = 1; ar[1] = 1.0; # a[1,1] = 1
ia[2] = 1; ja[2] = 2; ar[2] = 1.0; # a[1,2] = 1
ia[3] = 1; ja[3] = 3; ar[3] = 1.0; # a[1,3] = 1
ia[4] = 2; ja[4] = 1; ar[4] = 10.0; # a[2,1] = 10
ia[5] = 3; ja[5] = 1; ar[5] = 2.0; # a[3,1] = 2
ia[6] = 2; ja[6] = 2; ar[6] = 4.0; # a[2,2] = 4
ia[7] = 3; ja[7] = 2; ar[7] = 2.0; # a[3,2] = 2
ia[8] = 2; ja[8] = 3; ar[8] = 5.0; # a[2,3] = 5
ia[9] = 3; ja[9] = 3; ar[9] = 6.0; # a[3,3] = 6
glp_load_matrix(lp, 9, ia, ja, ar);
glp_simplex(lp, None);
Z = glp_get_obj_val(lp);
x1 = glp_get_col_prim(lp, 1);
x2 = glp_get_col_prim(lp, 2);
x3 = glp_get_col_prim(lp, 3);
print("\nZ = %g; x1 = %g; x2 = %g; x3 = %g\n" % (Z, x1, x2, x3))
glp_delete_prob(lp);
... will produce the following output (the example can also be found at
examples/example.py):
::
GLPK Simplex Optimizer, v4.52
3 rows, 3 columns, 9 non-zeros
* 0: obj = 0.000000000e+00 infeas = 0.000e+00 (0)
* 2: obj = 7.333333333e+02 infeas = 0.000e+00 (0)
OPTIMAL LP SOLUTION FOUND
Z = 733.333; x1 = 33.3333; x2 = 66.6667; x3 = 0
Pretty ugly right? Consider using `optlang <https://github.com/biosustain/optlang>`__ for formulating and solving your optimization problems.
Documentation
~~~~~~~~~~~~~
You can find documentation on GLPK's C API `here <http://kam.mff.cuni.cz/~elias/glpk.pdf>`__
Development
~~~~~~~~~~~
You still want to install it from source? Then you'll need to install the following
dependencies first.
- GLPK
- swig
If you're on OS X, swig and GLPK can easily be installed with
`homebrew <http://brew.sh/>`__.
::
brew install swig glpk
If you're using ubuntu linux, you can install swig and GLPK using
``apt-get``.
::
apt-get install glpk-utils libglpk-dev swig
If you're on Windows, you are on your own (checkout the `appveyor.yml <https://github.com/biosustain/swiglpk/blob/master/appveyor.yml>`_ config file for directions).
Then clone the repo and run the following.
::
python setup.py install
.. |PyPI| image:: https://img.shields.io/pypi/v/swiglpk.svg
:target: https://pypi.python.org/pypi/swiglpk
.. |License| image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg
:target: http://www.gnu.org/licenses/gpl-3.0
.. |Build Status| image:: https://github.com/biosustain/swiglpk/actions/workflows/main.yml/badge.svg
:target: https://github.com/biosustain/swiglpk/actions/workflows/main.yml
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"author": "Nikolaus Sonnenschein",
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"description": "swiglpk\n=======\n\n*Plain python bindings for the GNU Linear Programming Kit (GLPK)*\n\n|PyPI| |License| |Build Status|\n\nWhy?\n~~~~\n\n*swiglpk* is not a high-level wrapper for GLPK (take a look at\n`optlang <https://github.com/biosustain/optlang>`__ if you are\ninterested in a python-based mathematical programming language). It just\nprovides plain vanilla `swig <http://www.swig.org/>`__ bindings to the\nunderlying C library. In constrast to other GLPK wrappers for python\n(e.g. `PyGLPK <http://tfinley.net/software/pyglpk/>`__,\n`Python-GLPK <http://www.dcc.fc.up.pt/~jpp/code/python-glpk/>`__,\n`ctypes-glpk <https://code.google.com/p/ctypes-glpk/>`__,\n`ecyglpki <https://github.com/equaeghe/ecyglpki>`__ etc.) it is fairly\nversion agnostic: it will try to guess the location of the glpk.h header\nfile (using ``which glpsol``) and then compile the extension for your\nparticular GLPK installation. Furthermore, swiglpk provides binary wheels\nfor all major platforms, which are always up-to-date with the most\nrecent GLPK version (swiglpk versions follow GLPK versioning in the major\nand minor version digits to emphasize that).\n\nPlease show us some love by staring this repo if you find swiglpk useful!\n\nInstallation\n~~~~~~~~~~~~\n\n::\n\n pip install swiglpk\n\nThat's it. swiglpk comes with binary wheels for Windows, Mac, and Linux. No installation of third-party dependencies necessary.\n\nExample\n~~~~~~~\n\nRunning the following (slightly adapted) example from the `GLPK\nmanual <http://kam.mff.cuni.cz/~elias/glpk.pdf>`__ ...\n\n::\n\n from swiglpk import *\n\n ia = intArray(1+1000); ja = intArray(1+1000);\n ar = doubleArray(1+1000);\n lp = glp_create_prob();\n glp_set_prob_name(lp, \"sample\");\n glp_set_obj_dir(lp, GLP_MAX);\n glp_add_rows(lp, 3);\n glp_set_row_name(lp, 1, \"p\");\n glp_set_row_bnds(lp, 1, GLP_UP, 0.0, 100.0);\n glp_set_row_name(lp, 2, \"q\");\n glp_set_row_bnds(lp, 2, GLP_UP, 0.0, 600.0);\n glp_set_row_name(lp, 3, \"r\");\n glp_set_row_bnds(lp, 3, GLP_UP, 0.0, 300.0);\n glp_add_cols(lp, 3);\n glp_set_col_name(lp, 1, \"x1\");\n glp_set_col_bnds(lp, 1, GLP_LO, 0.0, 0.0);\n glp_set_obj_coef(lp, 1, 10.0);\n glp_set_col_name(lp, 2, \"x2\");\n glp_set_col_bnds(lp, 2, GLP_LO, 0.0, 0.0);\n glp_set_obj_coef(lp, 2, 6.0);\n glp_set_col_name(lp, 3, \"x3\");\n glp_set_col_bnds(lp, 3, GLP_LO, 0.0, 0.0);\n glp_set_obj_coef(lp, 3, 4.0);\n ia[1] = 1; ja[1] = 1; ar[1] = 1.0; # a[1,1] = 1\n ia[2] = 1; ja[2] = 2; ar[2] = 1.0; # a[1,2] = 1\n ia[3] = 1; ja[3] = 3; ar[3] = 1.0; # a[1,3] = 1\n ia[4] = 2; ja[4] = 1; ar[4] = 10.0; # a[2,1] = 10\n ia[5] = 3; ja[5] = 1; ar[5] = 2.0; # a[3,1] = 2\n ia[6] = 2; ja[6] = 2; ar[6] = 4.0; # a[2,2] = 4\n ia[7] = 3; ja[7] = 2; ar[7] = 2.0; # a[3,2] = 2\n ia[8] = 2; ja[8] = 3; ar[8] = 5.0; # a[2,3] = 5\n ia[9] = 3; ja[9] = 3; ar[9] = 6.0; # a[3,3] = 6\n glp_load_matrix(lp, 9, ia, ja, ar);\n glp_simplex(lp, None);\n Z = glp_get_obj_val(lp);\n x1 = glp_get_col_prim(lp, 1);\n x2 = glp_get_col_prim(lp, 2);\n x3 = glp_get_col_prim(lp, 3);\n print(\"\\nZ = %g; x1 = %g; x2 = %g; x3 = %g\\n\" % (Z, x1, x2, x3))\n glp_delete_prob(lp);\n\n... will produce the following output (the example can also be found at\nexamples/example.py):\n\n::\n\n GLPK Simplex Optimizer, v4.52\n 3 rows, 3 columns, 9 non-zeros\n * 0: obj = 0.000000000e+00 infeas = 0.000e+00 (0)\n * 2: obj = 7.333333333e+02 infeas = 0.000e+00 (0)\n OPTIMAL LP SOLUTION FOUND\n\n Z = 733.333; x1 = 33.3333; x2 = 66.6667; x3 = 0\n\nPretty ugly right? Consider using `optlang <https://github.com/biosustain/optlang>`__ for formulating and solving your optimization problems.\n\nDocumentation\n~~~~~~~~~~~~~\n\nYou can find documentation on GLPK's C API `here <http://kam.mff.cuni.cz/~elias/glpk.pdf>`__\n\nDevelopment\n~~~~~~~~~~~\n\nYou still want to install it from source? Then you'll need to install the following\ndependencies first.\n\n- GLPK\n- swig\n\nIf you're on OS X, swig and GLPK can easily be installed with\n`homebrew <http://brew.sh/>`__.\n\n::\n\n brew install swig glpk\n\nIf you're using ubuntu linux, you can install swig and GLPK using\n``apt-get``.\n\n::\n\n apt-get install glpk-utils libglpk-dev swig\n\nIf you're on Windows, you are on your own (checkout the `appveyor.yml <https://github.com/biosustain/swiglpk/blob/master/appveyor.yml>`_ config file for directions).\n\nThen clone the repo and run the following.\n::\n\n python setup.py install\n\n\n.. |PyPI| image:: https://img.shields.io/pypi/v/swiglpk.svg\n :target: https://pypi.python.org/pypi/swiglpk\n.. |License| image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg\n :target: http://www.gnu.org/licenses/gpl-3.0\n.. |Build Status| image:: https://github.com/biosustain/swiglpk/actions/workflows/main.yml/badge.svg\n :target: https://github.com/biosustain/swiglpk/actions/workflows/main.yml\n",
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