Metadata-Version: 2.1
Name: Py-BOBYQA
Version: 1.3
Summary: A flexible derivative-free solver for (bound constrained) general objective minimization
Home-page: https://github.com/numericalalgorithmsgroup/pybobyqa/
Author: Lindon Roberts
Author-email: lindon.roberts@maths.ox.ac.uk
License: GNU GPL
Download-URL: https://github.com/numericalalgorithmsgroup/pybobyqa/archive/v1.3.tar.gz
Description: ====================================================================
        Py-BOBYQA: Derivative-Free Solver for Bound-Constrained Minimization
        ====================================================================
        
        .. image::  https://travis-ci.org/numericalalgorithmsgroup/pybobyqa.svg?branch=master
           :target: https://travis-ci.org/numericalalgorithmsgroup/pybobyqa
           :alt: Build Status
        
        .. image::  https://img.shields.io/badge/License-GPL%20v3-blue.svg
           :target: https://www.gnu.org/licenses/gpl-3.0
           :alt: GNU GPL v3 License
        
        .. image:: https://img.shields.io/pypi/v/Py-BOBYQA.svg
           :target: https://pypi.python.org/pypi/Py-BOBYQA
           :alt: Latest PyPI version
        
        .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2630437.svg
           :target: https://doi.org/10.5281/zenodo.2630437
           :alt: DOI:10.5281/zenodo.2630437
        
        Py-BOBYQA is a flexible package for solving bound-constrained general objective minimization, without requiring derivatives of the objective. At its core, it is a Python implementation of the BOBYQA algorithm by Powell, but Py-BOBYQA has extra features improving its performance on some problems (see the papers below for details). Py-BOBYQA is particularly useful when evaluations of the objective function are expensive and/or noisy.
        
        More details about Py-BOBYQA and its enhancements over BOBYQA can be found in our papers:
        
        1. Coralia Cartis, Jan Fiala, Benjamin Marteau and Lindon Roberts, `Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers <https://doi.org/10.1145/3338517>`_, *ACM Transactions on Mathematical Software*, 45:3 (2019), pp. 32:1-32:41 [`arXiv preprint: 1804.00154 <https://arxiv.org/abs/1804.00154>`_] 
        2. Coralia Cartis, Lindon Roberts and Oliver Sheridan-Methven, `Escaping local minima with derivative-free methods: a numerical investigation <https://doi.org/10.1080/02331934.2021.1883015>`_, *Optimization* (2021). [`arXiv preprint: 1812.11343 <https://arxiv.org/abs/1812.11343>`_] 
        
        Please 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.
        
        The 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>`_.
        
        If 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.
        
        Documentation
        -------------
        See manual.pdf or the `online manual <https://numericalalgorithmsgroup.github.io/pybobyqa/>`_.
        
        Citation
        --------
        If you use Py-BOBYQA in a paper, please cite:
        
        Cartis, C., Fiala, J., Marteau, B. and Roberts, L., Improving the Flexibility and Robustness of Model-Based Derivative-Free Optimization Solvers, *ACM Transactions on Mathematical Software*, 45:3 (2019), pp. 32:1-32:41.
        
        If you use Py-BOBYQA's global optimization heuristic, please cite the above and also
        
        Cartis, C., Roberts, L. and Sheridan-Methven, O., Escaping local minima with derivative-free methods: a numerical investigation, Optimization, (2021). 
        
        Requirements
        ------------
        Py-BOBYQA requires the following software to be installed:
        
        * Python 2.7 or Python 3 (http://www.python.org/)
        
        Additionally, the following python packages should be installed (these will be installed automatically if using *pip*, see `Installation using pip`_):
        
        * NumPy 1.11 or higher (http://www.numpy.org/)
        * SciPy 0.18 or higher (http://www.scipy.org/)
        * Pandas 0.17 or higher (http://pandas.pydata.org/)
        
        **Optional package:** Py-BOBYQA versions 1.2 and higher also support the `trustregion <https://github.com/lindonroberts/trust-region>`_ package for fast trust-region subproblem solutions. To install this, make sure you have a Fortran compiler (e.g. `gfortran <https://gcc.gnu.org/wiki/GFortran>`_) and NumPy installed, then run :code:`pip install trustregion`. You do not have to have trustregion installed for Py-BOBYQA to work, and it is not installed by default.
        
        Installation using pip
        ----------------------
        For easy installation, use `pip <http://www.pip-installer.org/>`_ as root:
        
         .. code-block:: bash
        
            $ [sudo] pip install Py-BOBYQA
        
        or alternatively *easy_install*:
        
         .. code-block:: bash
        
            $ [sudo] easy_install Py-BOBYQA
        
        If you do not have root privileges or you want to install Py-BOBYQA for your private use, you can use:
        
         .. code-block:: bash
        
            $ pip install --user Py-BOBYQA
        
        which will install Py-BOBYQA in your home directory.
        
        Note that if an older install of Py-BOBYQA is present on your system you can use:
        
         .. code-block:: bash
        
            $ [sudo] pip install --upgrade Py-BOBYQA
        
        to upgrade Py-BOBYQA to the latest version.
        
        Manual installation
        -------------------
        Alternatively, you can download the source code from `Github <https://github.com/numericalalgorithmsgroup/pybobyqa>`_ and unpack as follows:
        
         .. code-block:: bash
        
            $ git clone https://github.com/numericalalgorithmsgroup/pybobyqa
            $ cd pybobyqa
        
        Py-BOBYQA is written in pure Python and requires no compilation. It can be installed using:
        
         .. code-block:: bash
        
            $ [sudo] pip install .
        
        If you do not have root privileges or you want to install Py-BOBYQA for your private use, you can use:
        
         .. code-block:: bash
        
            $ pip install --user .
        
        instead.
        
        To 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:
        
         .. code-block:: bash
        
            $ git pull
            $ [sudo] pip install .  # with admin privileges
        
        Testing
        -------
        If you installed Py-BOBYQA manually, you can test your installation by running:
        
         .. code-block:: bash
        
            $ python setup.py test
        
        Alternatively, the HTML documentation provides some simple examples of how to run Py-BOBYQA.
        
        Examples
        --------
        Examples of how to run Py-BOBYQA are given in the `online documentation <https://numericalalgorithmsgroup.github.io/pybobyqa/>`_, and the `examples directory <https://github.com/numericalalgorithmsgroup/pybobyqa/tree/master/examples>`_ in Github.
        
        Uninstallation
        --------------
        If Py-BOBYQA was installed using *pip* you can uninstall as follows:
        
         .. code-block:: bash
        
            $ [sudo] pip uninstall Py-BOBYQA
        
        If Py-BOBYQA was installed manually you have to remove the installed files by hand (located in your python site-packages directory).
        
        Bugs
        ----
        Please report any bugs using GitHub's issue tracker.
        
        License
        -------
        This algorithm is released under the GNU GPL license. Please `contact NAG <http://www.nag.com/content/worldwide-contact-information>`_ for alternative licensing.
        
Keywords: mathematics derivative free optimization
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Framework :: IPython
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Provides-Extra: trustregion
