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        "author": "Fabian Pedregosa",
        "author_email": "f@bianp.net",
        "bugtrack_url": null,
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            "License :: OSI Approved :: BSD License",
            "Operating System :: POSIX",
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            "Programming Language :: Python :: 2",
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            "Topic :: Software Development"
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        "description": ".. image:: https://travis-ci.org/fabianp/gdprox.svg?branch=master\n    :target: https://travis-ci.org/fabianp/gdprox\n\ngdprox, proximal gradient-descent algorithms\n============================================\n\nImplements the proximal gradient-descent algorithm for composite objective functions, i.e. functions of the form :code:`f(x) + g(x)`, where f is a smooth function and g is a possibly non-smooth function for which the proximal operator is known. \n\nThe main function in this package is :code:`gdprox.fmin_cgprox`. This function follows a similar interface than the functions in :code:`scipy.optimize`. The definition of this function is:\n\n\n.. code-block:: python\n\n\tdef fmin_cgprox(f, fprime, g_prox, x0, rtol=1e-6,\n\t                maxiter=1000, verbose=0, default_step_size=1.):\n\t    \"\"\"\n\t    proximal gradient-descent solver for optimization problems of the form\n\n\t                       minimize_x f(x) + g(x)\n\n\t    where f is a smooth function and g is a (possibly non-smooth)\n\t    function for which the proximal operator is known.\n\n\t    Parameters\n\t    ----------\n\t    f : callable\n\t        f(x) returns the value of f at x.\n\n\t    f_prime : callable\n\t        f_prime(x) returns the gradient of f.\n\n\t    g_prox : callable of the form g_prox(x, alpha)\n\t        g_prox(x, alpha) returns the proximal operator of g at x\n\t        with parameter alpha.\n\n\t    x0 : array-like\n\t        Initial guess\n\n\t    maxiter : int\n\t        Maximum number of iterations.\n\n\t    verbose : int\n\t        Verbosity level, from 0 (no output) to 2 (output on each iteration)\n\n\t    default_step_size : float\n\t        Starting value for the line-search procedure.\n\n\t    Returns\n\t    -------\n\t    res : OptimizeResult\n\t        The optimization result represented as a\n\t        ``scipy.optimize.OptimizeResult`` object. Important attributes are:\n\t        ``x`` the solution array, ``success`` a Boolean flag indicating if\n\t        the optimizer exited successfully and ``message`` which describes\n\t        the cause of the termination. See `scipy.optimize.OptimizeResult`\n\t        for a description of other attributes.\n\t    \"\"\"",
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