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        "author": "Lindon Roberts",
        "author_email": "lindon.roberts@maths.ox.ac.uk",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Environment :: Console",
            "Framework :: IPython",
            "Framework :: Jupyter",
            "Intended Audience :: Financial and Insurance Industry",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License (GPL)",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Mathematics"
        ],
        "description": "===================================================\nDFO-LS: Derivative-Free Optimizer for Least-Squares\n===================================================\n\n.. image::  https://travis-ci.org/numericalalgorithmsgroup/dfols.svg?branch=master\n   :target: https://travis-ci.org/numericalalgorithmsgroup/dfols\n   :alt: Build Status\n\n.. image::  https://img.shields.io/badge/License-GPL%20v3-blue.svg\n   :target: https://www.gnu.org/licenses/gpl-3.0\n   :alt: GNU GPL v3 License\n\n.. image:: https://img.shields.io/pypi/v/DFO-LS.svg\n   :target: https://pypi.python.org/pypi/DFO-LS\n   :alt: Latest PyPI version\n\nDFO-LS is a flexible package for solving nonlinear least-squares minimisation, without requiring derivatives of the objective. It is particularly useful when evaluations of the objective function are expensive and/or noisy.\n\nThis is an implementation of the algorithm from our paper: C. Cartis, J. Fiala, B. Marteau and L. Roberts, `Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers <https://arxiv.org/abs/1804.00154>`_, technical report, University of Oxford, (2018). For reproducibility of all figures in this paper, please feel free to contact the authors. DFO-LS is more flexible version of `DFO-GN <https://github.com/numericalalgorithmsgroup/dfogn>`_.\n\nIf you are interested in solving general optimization problems (without a least-squares structure), you may wish to try `Py-BOBYQA <https://github.com/numericalalgorithmsgroup/pybobyqa>`_, which has many of the same features as DFO-LS.\n\nDocumentation\n-------------\nSee manual.pdf or `here <https://numericalalgorithmsgroup.github.io/dfols/>`_.\n\nRequirements\n------------\nDFO-LS requires the following software to be installed:\n\n* Python 2.7 or Python 3 (http://www.python.org/)\n\nAdditionally, the following python packages should be installed (these will be installed automatically if using *pip*, see `Installation using pip`_):\n\n* NumPy 1.11 or higher (http://www.numpy.org/)\n* SciPy 0.18 or higher (http://www.scipy.org/)\n* Pandas 0.17 or higher (http://pandas.pydata.org/)\n\nInstallation using pip\n----------------------\nFor easy installation, use `pip <http://www.pip-installer.org/>`_ as root:\n\n .. code-block:: bash\n\n    $ [sudo] pip install DFO-LS\n\nor alternatively *easy_install*:\n\n .. code-block:: bash\n\n    $ [sudo] easy_install DFO-LS\n\nIf you do not have root privileges or you want to install DFO-LS for your private use, you can use:\n\n .. code-block:: bash\n\n    $ pip install --user DFO-LS\n\nwhich will install DFO-LS in your home directory.\n\nNote that if an older install of DFO-LS is present on your system you can use:\n\n .. code-block:: bash\n\n    $ [sudo] pip install --upgrade DFO-LS\n\nto upgrade DFO-LS to the latest version.\n\nManual installation\n-------------------\nAlternatively, you can download the source code from `Github <https://github.com/numericalalgorithmsgroup/dfols>`_ and unpack as follows:\n\n .. code-block:: bash\n\n    $ git clone https://github.com/numericalalgorithmsgroup/dfols\n    $ cd dfols\n\nDFO-LS is written in pure Python and requires no compilation. It can be installed using:\n\n .. code-block:: bash\n\n    $ [sudo] pip install .\n\nIf you do not have root privileges or you want to install DFO-LS for your private use, you can use:\n\n .. code-block:: bash\n\n    $ pip install --user .\n\ninstead.\n\nTo upgrade DFO-LS to the latest version, navigate to the top-level directory (i.e. the one containing :code:`setup.py`) and rerun the installation using :code:`pip`, as above:\n\n .. code-block:: bash\n\n    $ git pull\n    $ [sudo] pip install .  # with admin privileges\n\nTesting\n-------\nIf you installed DFO-LS manually, you can test your installation by running:\n\n .. code-block:: bash\n\n    $ python setup.py test\n\nAlternatively, the HTML documentation provides some simple examples of how to run DFO-LS.\n\nExamples\n--------\nExamples of how to run DFO-LS are given in the `documentation <https://numericalalgorithmsgroup.github.io/dfols/>`_, and the `examples <https://github.com/numericalalgorithmsgroup/dfols/tree/master/examples>`_ directory in Github.\n\nUninstallation\n--------------\nIf DFO-LS was installed using *pip* you can uninstall as follows:\n\n .. code-block:: bash\n\n    $ [sudo] pip uninstall DFO-LS\n\nIf DFO-LS was installed manually you have to remove the installed files by hand (located in your python site-packages directory).\n\nBugs\n----\nPlease report any bugs using GitHub's issue tracker.\n\nLicense\n-------\nThis algorithm is released under the GNU GPL license. Please `contact NAG <http://www.nag.com/content/worldwide-contact-information>`_ for alternative licensing.",
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