Metadata-Version: 2.1
Name: optlang
Version: 1.4.6
Summary: Formulate optimization problems using sympy expressions and solve them using interfaces to third-party optimization software (e.g. GLPK).
Home-page: https://github.com/opencobra/optlang
Author: Nikolaus Sonnenschein
Author-email: niko.sonnenschein@gmail.com
License: Apache-2.0
Download-URL: https://pypi.org/project/optlang/
Project-URL: Source Code, https://github.com/opencobra/optlang
Project-URL: Documentation, https://optlang.readthedocs.io
Project-URL: Bug Tracker, https://github.com/opencobra/optlang/issues
Description: optlang
        =======
        
        *Sympy based mathematical programming language*
        
        |PyPI| |Python Versions| |License| |Code of Conduct| |GitHub Actions| |Coverage Status| |Documentation Status| |Gitter| |JOSS| |DOI|
        
        Optlang is a Python package for solving mathematical optimization
        problems, i.e. maximizing or minimizing an objective function over a set
        of variables subject to a number of constraints. Optlang provides a
        common interface to a series of optimization tools, so different solver
        backends can be changed in a transparent way.
        Optlang's object-oriented API takes advantage of the symbolic math library
        `sympy <http://sympy.org/en/index.html>`__ to allow objective functions
        and constraints to be easily formulated from symbolic expressions of
        variables (see examples).
        
        Show us some love by staring this repo if you find optlang useful!
        
        Also, please use the GitHub `issue tracker <https://github.com/biosustain/optlang/issues>`_
        to 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.
        
        Installation
        ~~~~~~~~~~~~
        
        Install using pip
        
        ::
        
            pip install optlang
        
        This 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/>`_.
        Quadratic programming (and MIQP) is supported through additional optional solvers (see below).
        
        Dependencies
        ~~~~~~~~~~~~
        
        The following dependencies are needed.
        
        -  `sympy >= 1.0.0 <http://sympy.org/en/index.html>`__
        -  `six >= 1.9.0 <https://pypi.python.org/pypi/six>`__
        -  `swiglpk >= 1.4.3 <https://pypi.python.org/pypi/swiglpk>`__
        
        The following are optional dependencies that allow other solvers to be used.
        
        -  `cplex <https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/>`__ (LP, MILP, QP, MIQP)
        -  `gurobipy <http://www.gurobi.com>`__ (LP, MILP, QP, MIQP)
        -  `scipy <http://www.scipy.org>`__ (LP)
        
        
        
        Example
        ~~~~~~~
        
        Formulating and solving the problem is straightforward (example taken
        from `GLPK documentation <http://www.gnu.org/software/glpk>`__):
        
        .. code-block:: python
        
            from __future__ import print_function
            from optlang import Model, Variable, Constraint, Objective
        
            # All the (symbolic) variables are declared, with a name and optionally a lower and/or upper bound.
            x1 = Variable('x1', lb=0)
            x2 = Variable('x2', lb=0)
            x3 = Variable('x3', lb=0)
        
            # A constraint is constructed from an expression of variables and a lower and/or upper bound (lb and ub).
            c1 = Constraint(x1 + x2 + x3, ub=100)
            c2 = Constraint(10 * x1 + 4 * x2 + 5 * x3, ub=600)
            c3 = Constraint(2 * x1 + 2 * x2 + 6 * x3, ub=300)
        
            # An objective can be formulated
            obj = Objective(10 * x1 + 6 * x2 + 4 * x3, direction='max')
        
            # Variables, constraints and objective are combined in a Model object, which can subsequently be optimized.
            model = Model(name='Simple model')
            model.objective = obj
            model.add([c1, c2, c3])
        
            status = model.optimize()
        
            print("status:", model.status)
            print("objective value:", model.objective.value)
            print("----------")
            for var_name, var in model.variables.iteritems():
                print(var_name, "=", var.primal)
        
        The example will produce the following output:
        
        ::
        
            status: optimal
            objective value: 733.333333333
            ----------
            x2 = 66.6666666667
            x3 = 0.0
            x1 = 33.3333333333
        
        Using a particular solver
        -------------------------
        If you have more than one solver installed, it's also possible to specify which one to use, by importing directly from the
        respective solver interface, e.g. :code:`from optlang.glpk_interface import Model, Variable, Constraint, Objective`
        
        Documentation
        ~~~~~~~~~~~~~
        
        Documentation for optlang is provided at
        `readthedocs.org <http://optlang.readthedocs.org/en/latest/>`__.
        
        Citation
        ~~~~~~~~
        
        Please 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).
        
        Contributing
        ~~~~~~~~~~~~
        
        Please read `<CONTRIBUTING.md>`__.
        
        Funding
        ~~~~~~~
        
        The development of optlang was partly support by the Novo Nordisk Foundation.
        
        Future outlook
        ~~~~~~~~~~~~~~
        
        -  `Mosek <http://www.mosek.com/>`__ interface (provides academic
           licenses)
        -  `GAMS <http://www.gams.com/>`__ output (support non-linear problem
           formulation)
        -  `DEAP <https://code.google.com/p/deap/>`__ (support for heuristic
           optimization)
        -  Interface to `NEOS <http://www.neos-server.org/neos/>`__ optimization
           server (for testing purposes and solver evaluation)
        -  Automatically handle fractional and absolute value problems when
           dealing with LP/MILP/QP solvers (like GLPK,
           `CPLEX <http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/>`__
           etc.)
        
        .. |PyPI| image:: https://img.shields.io/pypi/v/optlang.svg
           :target: https://pypi.org/project/optlang/
           :alt: Current PyPI Version
        .. |Python Versions| image:: https://img.shields.io/pypi/pyversions/optlang.svg
           :target: https://pypi.org/project/optlang/
           :alt: Supported Python Versions
        .. |License| image:: https://img.shields.io/pypi/l/optlang.svg
           :target: https://www.apache.org/licenses/LICENSE-2.0
           :alt: Apache Software License Version 2.0
        .. |Code of Conduct| image:: https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg
           :target: .github/CODE_OF_CONDUCT.md
           :alt: Code of Conduct
        .. |GitHub Actions| image:: https://github.com/opencobra/optlang/workflows/CI-CD/badge.svg
           :target: https://github.com/opencobra/optlang/workflows/CI-CD
           :alt: GitHub Actions
        .. |Coverage Status| image:: https://codecov.io/gh/opencobra/optlang/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/opencobra/optlang
           :alt: Codecov
        .. |Documentation Status| image:: https://readthedocs.org/projects/optlang/badge/?version=latest
           :target: https://readthedocs.org/projects/optlang/?badge=latest
           :alt: Documentation Status
        .. |JOSS|  image:: http://joss.theoj.org/papers/cd848071a664d696e214a3950c840e15/status.svg
           :target: http://joss.theoj.org/papers/cd848071a664d696e214a3950c840e15
           :alt: Publication
        .. |DOI| image:: https://zenodo.org/badge/5031/biosustain/optlang.svg
           :target: https://zenodo.org/badge/latestdoi/5031/biosustain/optlang
           :alt: Zenodo Source Code
        .. |Gitter| image:: https://badges.gitter.im/biosustain/optlang.svg
           :target: https://gitter.im/biosustain/optlang?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
           :alt: Join the chat at https://gitter.im/biosustain/optlang
        
        
Keywords: optimization,mathematical programming,heuristic optimization,sympy
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: !=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7
Description-Content-Type: text/x-rst
Provides-Extra: development
