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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": "====================================================================\nPy-BOBYQA: Derivative-Free Solver for Bound-Constrained Minimization\n====================================================================\n\n.. image::  https://travis-ci.org/numericalalgorithmsgroup/pybobyqa.svg?branch=master\n   :target: https://travis-ci.org/numericalalgorithmsgroup/pybobyqa\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/Py-BOBYQA.svg\n   :target: https://pypi.python.org/pypi/Py-BOBYQA\n   :alt: Latest PyPI version\n\nPy-BOBYQA is a flexible package for solving bound-constrained general objective minimization, without requiring derivatives of the objective. It is a Python implementation of the BOBYQA algorithm by Powell. Py-BOBYQA is particularly useful when evaluations of the objective function are expensive and/or noisy.\n\nMore details about Py-BOBYQA can be found in our papers: \n\n1. Coralia Cartis, Jan Fiala, Benjamina Marteau and Lindon 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). \n2. Coralia Cartis, Lindon Roberts and Oliver Sheridan-Methven, `Escaping local minima with derivative-free methods: a numerical investigation <https://arxiv.org/abs/1812.11343>`_, technical report, University of Oxford, (2018). \n\nPlease cite [1] when using Py-BOBYQA for local optimization, and [1,2] when using Py-BOBYQA's global optimization heuristic functionality. For reproducibility of all figures, please feel free to contact the authors.\n\nThe original paper by Powell is: M. J. D. Powell, The BOBYQA algorithm for bound constrained optimization without derivatives, technical report DAMTP 2009/NA06, University of Cambridge (2009), and the original Fortran implementation is available `here <http://mat.uc.pt/~zhang/software.html>`_.\n\nIf you are interested in solving least-squares minimization problems, you may wish to try `DFO-LS <https://github.com/numericalalgorithmsgroup/dfols>`_, which has the same features as Py-BOBYQA (plus some more), and exploits the least-squares problem structure, so performs better on such problems.\n\nDocumentation\n-------------\nSee manual.pdf or `here <https://numericalalgorithmsgroup.github.io/pybobyqa/>`_.\n\nRequirements\n------------\nPy-BOBYQA 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 Py-BOBYQA\n\nor alternatively *easy_install*:\n\n .. code-block:: bash\n\n    $ [sudo] easy_install Py-BOBYQA\n\nIf you do not have root privileges or you want to install Py-BOBYQA for your private use, you can use:\n\n .. code-block:: bash\n\n    $ pip install --user Py-BOBYQA\n\nwhich will install Py-BOBYQA in your home directory.\n\nNote that if an older install of Py-BOBYQA is present on your system you can use:\n\n .. code-block:: bash\n\n    $ [sudo] pip install --upgrade Py-BOBYQA\n\nto upgrade Py-BOBYQA to the latest version.\n\nManual installation\n-------------------\nAlternatively, you can download the source code from `Github <https://github.com/numericalalgorithmsgroup/pybobyqa>`_ and unpack as follows:\n\n .. code-block:: bash\n\n    $ git clone https://github.com/numericalalgorithmsgroup/pybobyqa\n    $ cd pybobyqa\n\nPy-BOBYQA 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 Py-BOBYQA for your private use, you can use:\n\n .. code-block:: bash\n\n    $ pip install --user .\n\ninstead.\n\nTo upgrade Py-BOBYQA 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 Py-BOBYQA 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 Py-BOBYQA.\n\nExamples\n--------\nExamples of how to run Py-BOBYQA are given in the `documentation <https://numericalalgorithmsgroup.github.io/pybobyqa/>`_, and the `examples <https://github.com/numericalalgorithmsgroup/pybobyqa/tree/master/examples>`_ directory in Github.\n\nUninstallation\n--------------\nIf Py-BOBYQA was installed using *pip* you can uninstall as follows:\n\n .. code-block:: bash\n\n    $ [sudo] pip uninstall Py-BOBYQA\n\nIf Py-BOBYQA 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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