Metadata-Version: 2.1
Name: DFO-LS
Version: 1.3.0
Summary: A flexible derivative-free solver for (bound constrained) nonlinear least-squares minimization
Home-page: https://github.com/numericalalgorithmsgroup/dfols/
Author: Lindon Roberts
Author-email: lindon.roberts@anu.edu.au
License: GNU GPL
Download-URL: https://github.com/numericalalgorithmsgroup/dfols/archive/v1.3.0.tar.gz
Description: ===================================================
        DFO-LS: Derivative-Free Optimizer for Least-Squares
        ===================================================
        
        .. image::  https://github.com/numericalalgorithmsgroup/dfols/actions/workflows/python_testing.yml/badge.svg
           :target: https://github.com/numericalalgorithmsgroup/dfols/actions
           :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/DFO-LS.svg
           :target: https://pypi.python.org/pypi/DFO-LS
           :alt: Latest PyPI version
        
        .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2630426.svg
           :target: https://doi.org/10.5281/zenodo.2630426
           :alt: DOI:10.5281/zenodo.2630426
        
        DFO-LS is a flexible package for solving nonlinear least-squares minimization, without requiring derivatives of the objective. It is particularly useful when evaluations of the objective function are expensive and/or noisy. DFO-LS is more flexible version of `DFO-GN <https://github.com/numericalalgorithmsgroup/dfogn>`_.
        
        This 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://doi.org/10.1145/3338517>`_, *ACM Transactions on Mathematical Software*, 45:3 (2019), pp. 32:1-32:41 [`preprint <https://arxiv.org/abs/1804.00154>`_]. For reproducibility of all figures in this paper, please feel free to contact the authors. 
        
        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>`_, which has many of the same features as DFO-LS.
        
        Documentation
        -------------
        See manual.pdf or `here <https://numericalalgorithmsgroup.github.io/dfols/>`_.
        
        Citation
        --------
        If you use DFO-LS 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 <https://doi.org/10.1145/3338517>`_, *ACM Transactions on Mathematical Software*, 45:3 (2019), pp. 32:1-32:41.
        
        Requirements
        ------------
        DFO-LS 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:** DFO-LS 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 DFO-LS 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 DFO-LS
        
        or alternatively *easy_install*:
        
         .. code-block:: bash
        
            $ [sudo] easy_install DFO-LS
        
        If you do not have root privileges or you want to install DFO-LS for your private use, you can use:
        
         .. code-block:: bash
        
            $ pip install --user DFO-LS
        
        which will install DFO-LS in your home directory.
        
        Note that if an older install of DFO-LS is present on your system you can use:
        
         .. code-block:: bash
        
            $ [sudo] pip install --upgrade DFO-LS
        
        to upgrade DFO-LS to the latest version.
        
        Manual installation
        -------------------
        Alternatively, you can download the source code from `Github <https://github.com/numericalalgorithmsgroup/dfols>`_ and unpack as follows:
        
         .. code-block:: bash
        
            $ git clone https://github.com/numericalalgorithmsgroup/dfols
            $ cd dfols
        
        DFO-LS 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 DFO-LS for your private use, you can use:
        
         .. code-block:: bash
        
            $ pip install --user .
        
        instead.
        
        To 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:
        
         .. code-block:: bash
        
            $ git pull
            $ [sudo] pip install .  # with admin privileges
        
        Testing
        -------
        If you installed DFO-LS 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 DFO-LS.
        
        Examples
        --------
        Examples 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.
        
        Uninstallation
        --------------
        If DFO-LS was installed using *pip* you can uninstall as follows:
        
         .. code-block:: bash
        
            $ [sudo] pip uninstall DFO-LS
        
        If DFO-LS 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
Provides-Extra: trustregion
