{
    "info": {
        "author": "Stefan Endres, Carl Sandrock",
        "author_email": "stefan.c.endres@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Mathematics"
        ],
        "description": ".. image:: https://travis-ci.org/Stefan-Endres/tgo.svg?branch=master\n    :target: https://travis-ci.org/Stefan-Endres/tgo\n.. image:: https://coveralls.io/repos/github/Stefan-Endres/tgo/badge.png?branch=master\n    :target: https://coveralls.io/github/Stefan-Endres/tgo?branch=master\n\n\nDescription\n-----------\n\nFinds the global minimum of a function using topographical global\noptimisation (tgo_). Appropriate for solving general purpose NLP and blackbox\noptimisation problems to global optimality (low dimensional problems).\nThe general form of an optimisation problem is given by:\n\n.. _tgo: https://stefan-endres.github.io/tgo/\n\n::\n\n    minimize f(x) subject to\n\n    g_i(x) >= 0,  i = 1,...,m\n    h_j(x)  = 0,  j = 1,...,p\n\nwhere x is a vector of one or more variables. ``f(x)`` is the objective\nfunction ``R^n -> R``, ``g_i(x)`` are the inequality constraints.\n``h_j(x)`` are the equality constrains.\n\n\nInstallation\n------------\nStable:\n\n.. code::\n\n    $ pip install tgo\n\nLatest:\n\n.. code::\n\n    $ git clone https://bitbucket.org/upiamcompthermo/tgo\n    $ cd tgo\n    $ python setup.py install\n    $ python setup.py test\n\nDocumentation\n-------------\nThe docstrings and project website https://stefan-endres.github.io/tgo/ contains more detailed examples, notes and performance profiles.\n\nQuick example\n-------------\n\nConsider the problem of minimizing the Rosenbrock function. This function is implemented in ``rosen`` in ``scipy.optimize``\n\n.. code:: python\n\n    >>> from scipy.optimize import rosen\n    >>> from tgo import tgo\n    >>> bounds = [(0,2), (0, 2), (0, 2), (0, 2), (0, 2)]\n    >>> result = shgo(rosen, bounds)\n    >>> result.x, result.fun\n    (array([ 1.,  1.,  1.,  1.,  1.]), 2.9203923741900809e-18)\n\nNote that bounds determine the dimensionality of the objective function and is therefore a required input, however you can specify empty bounds using ``None`` or objects like numpy.inf which will be converted to large float numbers.\n\n\n\n",
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        "keywords": "optimization",
        "license": "MIT",
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        "name": "tgo",
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