Metadata-Version: 2.1
Name: DFO-LS
Version: 1.2
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@maths.ox.ac.uk
License: GNU GPL
Download-URL: https://github.com/numericalalgorithmsgroup/dfols/archive/v1.2.tar.gz
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
Requires-Dist: numpy (>=1.11)
Requires-Dist: scipy (>=0.18)
Requires-Dist: pandas (>=0.17)
Requires-Dist: trustregion (>=1.1)

===================================================
DFO-LS: Derivative-Free Optimizer for Least-Squares
===================================================

.. image::  https://travis-ci.org/numericalalgorithmsgroup/dfols.svg?branch=master
   :target: https://travis-ci.org/numericalalgorithmsgroup/dfols
   :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/)
* Fortran compiler (e.g. `gfortran <https://gcc.gnu.org/wiki/GFortran>`_), required by the `trustregion <https://github.com/lindonroberts/trust-region>`_ package.

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 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.


