Metadata-Version: 1.1
Name: pyunlocbox
Version: 0.5.2
Summary: Convex Optimization in Python using Proximal Splitting
Home-page: https://github.com/epfl-lts2/pyunlocbox
Author: EPFL LTS2
Author-email: UNKNOWN
License: BSD
Description: ==============================================
        PyUNLocBoX: Optimization by Proximal Splitting
        ==============================================
        
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           :target: https://pyunlocbox.readthedocs.io
        .. |pypi| image:: https://img.shields.io/pypi/v/pyunlocbox.svg
           :target: https://pypi.python.org/pypi/pyunlocbox
        .. |license| image:: https://img.shields.io/pypi/l/pyunlocbox.svg
           :target: https://github.com/epfl-lts2/pyunlocbox/blob/master/LICENSE.txt
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           :target: https://pypi.python.org/pypi/pyunlocbox
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           :target: https://travis-ci.org/epfl-lts2/pyunlocbox
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           :target: https://github.com/epfl-lts2/pyunlocbox
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           :target: https://mybinder.org/v2/gh/epfl-lts2/pyunlocbox/master?filepath=playground.ipynb
        
        The PyUNLocBoX is a Python package which uses
        `proximal splitting methods <https://en.wikipedia.org/wiki/Proximal_gradient_method>`_
        to solve non-differentiable convex optimization problems.
        It is a free software, distributed under the BSD license, and
        available on `PyPI <https://pypi.python.org/pypi/pyunlocbox>`_.
        The documentation is available on
        `Read the Docs <https://pyunlocbox.readthedocs.io>`_
        and development takes place on
        `GitHub <https://github.com/epfl-lts2/pyunlocbox>`_.
        (A `Matlab counterpart <https://lts2.epfl.ch/unlocbox>`_ exists.)
        
        The package is designed to be easy to use while allowing any advanced tasks. It
        is not meant to be a black-box optimization tool. You'll have to carefully
        design your solver. In exchange you'll get full control of what the package
        does for you, without the pain of rewriting the proximity operators and the
        solvers and with the added benefit of tested algorithms. With this package, you
        can focus on your problem and the best way to solve it rather that the details
        of the algorithms. It comes with the following solvers:
        
        * Gradient descent
        * Forward-backward splitting algorithm (FISTA, ISTA)
        * Douglas-Rachford splitting algorithm
        * Generalized forward-backward
        * Monotone+Lipschitz forward-backward-forward primal-dual algorithm
        * Projection-based primal-dual algorithm
        
        Moreover, the following acceleration schemes are included:
        
        * FISTA acceleration scheme
        * Backtracking based on a quadratic approximation of the objective
        * Regularized nonlinear acceleration (RNA)
        
        To compose your objective, you can either define your custom functions (which
        should implement an evaluation method and a gradient or proximity method) or
        use one of the followings:
        
        * L1-norm
        * L2-norm
        * TV-norm
        * Nuclear-norm
        * Projection on the L2-ball
        
        Following is a typical usage example who solves an optimization problem
        composed by the sum of two convex functions. The functions and solver objects
        are first instantiated with the desired parameters. The problem is then solved
        by a call to the solving function.
        
        >>> from pyunlocbox import functions, solvers
        >>> f1 = functions.norm_l2(y=[4, 5, 6, 7])
        >>> f2 = functions.dummy()
        >>> solver = solvers.forward_backward()
        >>> ret = solvers.solve([f1, f2], [0., 0, 0, 0], solver, atol=1e-5)
        Solution found after 9 iterations:
            objective function f(sol) = 6.714385e-08
            stopping criterion: ATOL
        >>> ret['sol']
        array([ 3.99990766,  4.99988458,  5.99986149,  6.99983841])
        
        You can
        `try it online <https://mybinder.org/v2/gh/epfl-lts2/pyunlocbox/master?filepath=playground.ipynb>`_,
        look at the
        `tutorials <https://pyunlocbox.readthedocs.io/en/stable/tutorials/index.html>`_
        to learn how to use it, or look at the
        `reference guide <https://pyunlocbox.readthedocs.io/en/stable/reference/index.html>`_
        for an exhaustive documentation of the API. Enjoy the package!
        
        Installation
        ------------
        
        The PyUNLocBoX is available on PyPI::
        
            $ pip install pyunlocbox
        
        Contributing
        ------------
        
        See the guidelines for contributing in ``CONTRIBUTING.rst``.
        
        Acknowledgments
        ---------------
        
        The PyUNLocBoX was started in 2014 as an academic open-source project for
        research purpose at the `EPFL LTS2 laboratory <https://lts2.epfl.ch>`_.
        
Keywords: convex optimization
Platform: any
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Environment :: Console
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
