Metadata-Version: 1.1
Name: dfbgn
Version: 0.1
Summary: A derivative-free solver for large-scale nonlinear least-squares minimization
Home-page: https://github.com/numericalalgorithmsgroup/dfbgn/
Author: Lindon Roberts
Author-email: lindon.roberts@anu.edu.au
License: GNU GPL
Download-URL: https://github.com/numericalalgorithmsgroup/dfbgn/archive/v0.1.tar.gz
Description: =========================================
        DFBGN: Derivative-Free Block Gauss-Newton
        =========================================
        
        .. image::  https://travis-ci.org/numericalalgorithmsgroup/dfbgn.svg?branch=master
           :target: https://travis-ci.org/numericalalgorithmsgroup/dfbgn
           :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
        
        DFBGN is a Python package for  nonlinear least-squares minimization, where derivatives are not available.
        It is particularly useful when evaluations of the objective are expensive and/or noisy, and the number of variables to be optimized is large.
        
        DFBGN is based on `DFO-LS <https://github.com/numericalalgorithmsgroup/dfols>`_, but is better-suited when there are many variables to be optimized (so the linear algebra in DFO-LS is too slow).
        Unlike DFO-LS, DFBGN does not currently support bound constraints on the variables.
        
        If 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>`_.
        
        Requirements
        ------------
        DFBGN 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/)
        
        Installation using pip
        ----------------------
        For easy installation, use `pip <http://www.pip-installer.org/>`_ as root:
        
         .. code-block:: bash
        
            $ [sudo] pip install dfbgn
        
        or alternatively *easy_install*:
        
         .. code-block:: bash
        
            $ [sudo] easy_install dfbgn
        
        If you do not have root privileges or you want to install DFBGN for your private use, you can use:
        
         .. code-block:: bash
        
            $ pip install --user dfbgn
        
        which will install DFBGN in your home directory.
        
        Note that if an older install of DFBGN is present on your system you can use:
        
         .. code-block:: bash
        
            $ [sudo] pip install --upgrade dfbgn
        
        to upgrade DFBGN to the latest version.
        
        Manual installation
        -------------------
        Alternatively, you can download the source code from `Github <https://github.com/numericalalgorithmsgroup/dfbgn>`_ and unpack as follows:
        
         .. code-block:: bash
        
            $ git clone https://github.com/numericalalgorithmsgroup/dfbgn
            $ cd dfbgn
        
        DFBGN 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 DFBGN for your private use, you can use:
        
         .. code-block:: bash
        
            $ pip install --user .
        
        instead.
        
        To upgrade DFBGN 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 DFBGN 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 DFBGN.
        
        Examples
        --------
        Examples of how to run DFBGN are given in the `documentation <https://numericalalgorithmsgroup.github.io/dfbgn/>`_, and the `examples <https://github.com/numericalalgorithmsgroup/dfbgn/tree/master/examples>`_ directory in Github.
        
        Uninstallation
        --------------
        If DFBGN was installed using *pip* you can uninstall as follows:
        
         .. code-block:: bash
        
            $ [sudo] pip uninstall dfbgn
        
        If DFBGN 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 nonlinear least squares
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
