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    "info": {
        "author": "Nikolaus Sonnenschein",
        "author_email": "niko.sonnenschein@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Topic :: Scientific/Engineering :: Mathematics"
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        "description": "optlang\n=======\n\n*Sympy based mathematical programming language*\n\n|PyPI| |License| |Travis| |Appveyor| |Coverage Status| |Documentation Status| |Gitter| |JOSS| |DOI|\n\nOptlang is a Python package for solving mathematical optimization\nproblems, i.e. maximizing or minimizing an objective function over a set\nof variables subject to a number of constraints. Optlang provides a\ncommon interface to a series of optimization tools, so different solver\nbackends can be changed in a transparent way.\nOptlang's object-oriented API takes advantage of the symbolic math library\n`sympy <http://sympy.org/en/index.html>`__ to allow objective functions\nand constraints to be easily formulated from symbolic expressions of\nvariables (see examples).\n\nShow us some love by staring this repo if you find optlang useful!\n\nAlso, please use the GitHub `issue tracker <https://github.com/biosustain/optlang/issues>`_\nto let us know about bugs or feature requests, or our `gitter channel <https://gitter.im/biosustain/optlang>`_ if you have problems or questions regarding optlang.\n\nInstallation\n~~~~~~~~~~~~\n\nInstall using pip\n\n::\n\n    pip install optlang\n\nThis will also install `swiglpk <https://github.com/biosustain/swiglpk>`_, an interface to the open source (mixed integer) LP solver `GLPK <https://www.gnu.org/software/glpk/>`_.\nQuadratic programming (and MIQP) is supported through additional optional solvers (see below).\n\nDependencies\n~~~~~~~~~~~~\n\nThe following dependencies are needed.\n\n-  `sympy >= 1.0.0 <http://sympy.org/en/index.html>`__\n-  `six >= 1.9.0 <https://pypi.python.org/pypi/six>`__\n-  `swiglpk >= 1.4.3 <https://pypi.python.org/pypi/swiglpk>`__\n\nThe following are optional dependencies that allow other solvers to be used.\n\n-  `cplex <https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/>`__ (LP, MILP, QP, MIQP)\n-  `gurobipy <http://www.gurobi.com>`__ (LP, MILP, QP, MIQP)\n-  `scipy <http://www.scipy.org>`__ (LP)\n\n\n\nExample\n~~~~~~~\n\nFormulating and solving the problem is straightforward (example taken\nfrom `GLPK documentation <http://www.gnu.org/software/glpk>`__):\n\n.. code-block:: python\n\n    from __future__ import print_function\n    from optlang import Model, Variable, Constraint, Objective\n\n    # All the (symbolic) variables are declared, with a name and optionally a lower and/or upper bound.\n    x1 = Variable('x1', lb=0)\n    x2 = Variable('x2', lb=0)\n    x3 = Variable('x3', lb=0)\n\n    # A constraint is constructed from an expression of variables and a lower and/or upper bound (lb and ub).\n    c1 = Constraint(x1 + x2 + x3, ub=100)\n    c2 = Constraint(10 * x1 + 4 * x2 + 5 * x3, ub=600)\n    c3 = Constraint(2 * x1 + 2 * x2 + 6 * x3, ub=300)\n\n    # An objective can be formulated\n    obj = Objective(10 * x1 + 6 * x2 + 4 * x3, direction='max')\n\n    # Variables, constraints and objective are combined in a Model object, which can subsequently be optimized.\n    model = Model(name='Simple model')\n    model.objective = obj\n    model.add([c1, c2, c3])\n\n    status = model.optimize()\n\n    print(\"status:\", model.status)\n    print(\"objective value:\", model.objective.value)\n    print(\"----------\")\n    for var_name, var in model.variables.iteritems():\n        print(var_name, \"=\", var.primal)\n\nThe example will produce the following output:\n\n::\n\n    status: optimal\n    objective value: 733.333333333\n    ----------\n    x2 = 66.6666666667\n    x3 = 0.0\n    x1 = 33.3333333333\n\nUsing a particular solver\n-------------------------\nIf you have more than one solver installed, it's also possible to specify which one to use, by importing directly from the\nrespective solver interface, e.g. :code:`from optlang.glpk_interface import Model, Variable, Constraint, Objective`\n\nDocumentation\n~~~~~~~~~~~~~\n\nDocumentation for optlang is provided at\n`readthedocs.org <http://optlang.readthedocs.org/en/latest/>`__.\n\nCitation\n~~~~~~~~\n\nPlease cite |JOSS| if you use optlang in a scientific publication. In case you would like to reference a specific version of of optlang you can also include the respective Zenodo DOI (|DOI| points to the latest version).\n\nContributing\n~~~~~~~~~~~~\n\nPlease read `<CONTRIBUTING.md>`__.\n\nFuture outlook\n~~~~~~~~~~~~~~\n\n-  `Mosek <http://www.mosek.com/>`__ interface (provides academic\n   licenses)\n-  `GAMS <http://www.gams.com/>`__ output (support non-linear problem\n   formulation)\n-  `DEAP <https://code.google.com/p/deap/>`__ (support for heuristic\n   optimization)\n-  Interface to `NEOS <http://www.neos-server.org/neos/>`__ optimization\n   server (for testing purposes and solver evaluation)\n-  Automatically handle fractional and absolute value problems when\n   dealing with LP/MILP/QP solvers (like GLPK,\n   `CPLEX <http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/>`__\n   etc.)\n\n.. |PyPI| image:: https://img.shields.io/pypi/v/optlang.svg?maxAge=2592000\n   :target: https://pypi.python.org/pypi/optlang\n.. |License| image:: http://img.shields.io/badge/license-APACHE2-blue.svg\n   :target: http://img.shields.io/badge/license-APACHE2-blue.svg\n.. |Travis| image:: https://img.shields.io/travis/biosustain/optlang/master.svg\n   :target: https://travis-ci.org/biosustain/optlang\n.. |Coverage Status| image:: https://img.shields.io/codecov/c/github/biosustain/optlang/master.svg\n   :target: https://codecov.io/gh/biosustain/optlang/branch/master\n.. |Documentation Status| image:: https://readthedocs.org/projects/optlang/badge/?version=latest\n   :target: https://readthedocs.org/projects/optlang/?badge=latest\n.. |JOSS|  image:: http://joss.theoj.org/papers/cd848071a664d696e214a3950c840e15/status.svg\n   :target: http://joss.theoj.org/papers/cd848071a664d696e214a3950c840e15\n.. |DOI| image:: https://zenodo.org/badge/5031/biosustain/optlang.svg\n   :target: https://zenodo.org/badge/latestdoi/5031/biosustain/optlang\n.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/443yp8hf25c6748h/branch/master?svg=true\n   :target: https://ci.appveyor.com/project/phantomas1234/optlang/branch/master\n.. |Gitter| image:: https://badges.gitter.im/biosustain/optlang.svg\n   :alt: Join the chat at https://gitter.im/biosustain/optlang\n   :target: https://gitter.im/biosustain/optlang?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge\n\n\n\n",
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