Metadata-Version: 1.1
Name: DFOGN
Version: 1.0.1
Summary: A simple derivative-free solver for (box constrained) nonlinear least-squares minimization
Home-page: https://github.com/numericalalgorithmsgroup/dfogn/
Author: Lindon Roberts
Author-email: lindon.roberts@maths.ox.ac.uk
License: GNU GPL
Download-URL: https://github.com/numericalalgorithmsgroup/dfogn/archive/1.0.1.tar.gz
Description: =====================================================================
        DFO-GN: Derivative-Free Nonlinear Least-Squares Solver |PyPI Version|
        =====================================================================
        DFO-GN is a package for solving nonlinear least-squares minimisation, without requiring derivatives of the objective.
        
        This is an implementation of the algorithm from our paper:
        `A Derivative-Free Gauss-Newton Method <https://arxiv.org/abs/1710.11005>`_, C. Cartis and L. Roberts, submitted (2017). For reproducibility of all figures in this paper, please feel free to contact the authors.
        
        Note: we have released a newer package, called DFO-LS, which is an upgrade of DFO-GN to improve its flexibility and robustness to noisy problems. See `here <https://github.com/numericalalgorithmsgroup/dfols>`_ for details.
        
        Documentation
        -------------
        See manual.pdf or `here <https://numericalalgorithmsgroup.github.io/dfogn/>`_.
        
        Requirements
        ------------
        DFO-GN 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 <http://www.pip-installer.org/>`_, see `Installation using pip`_):
        
        * `NumPy 1.11 or higher <http://www.numpy.org/>`_ 
        * `SciPy 0.18 or higher <http://www.scipy.org/>`_
        
        
        Installation using pip
        ----------------------
        For easy installation, use `pip <http://www.pip-installer.org/>`_ as root:
        
         .. code-block:: bash
        
            $ [sudo] pip install --pre dfogn
        
        If you do not have root privileges or you want to install DFO-GN for your private use, you can use:
        
         .. code-block:: bash
        
            $ pip install --pre --user dfogn
              
        which will install DFO-GN in your home directory.
        
        Note that if an older install of DFO-GN is present on your system you can use:
        
         .. code-block:: bash
        
            $ [sudo] pip install --pre --upgrade dfogn
              
        to upgrade DFO-GN to the latest version.
        
        Manual installation
        -------------------
        The source code for DFO-GN is `available on Github <https://https://github.com/numericalalgorithmsgroup/dfogn>`_:
        
         .. code-block:: bash
         
            $ git clone https://github.com/numericalalgorithmsgroup/dfogn
            $ cd dfogn
        
        DFO-GN is written in pure Python and requires no compilation. It can be installed using:
        
         .. code-block:: bash
        
            $ [sudo] pip install --pre .
        
        If you do not have root privileges or you want to install DFO-GN for your private use, you can use:
        
         .. code-block:: bash
        
            $ pip install --pre --user .
            
        instead.    
        
        Testing
        -------
        If you installed DFO-GN manually, you can test your installation by running:
        
         .. code-block:: bash
        
            $ python setup.py test
        
        Alternatively, the `documentation <https://numericalalgorithmsgroup.github.io/dfogn/>`_ provides some simple examples of how to run DFO-GN, which are also available in the examples directory.
        
        .. |PyPI Version| image:: https://img.shields.io/pypi/v/DFOGN.svg
                          :target: https://pypi.python.org/pypi/DFOGN
        
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
