===================================================
DFO-LS: Derivative-Free Optimizer for Least-Squares
===================================================
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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>`_.
The main algorithm is described in our paper [1] below.
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
--------
The development of DFO-LS is outlined over several publications:
1. 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 arXiv 1804.00154 <https://arxiv.org/abs/1804.00154>`_] .
2. M Hough and L Roberts, `Model-Based Derivative-Free Methods for Convex-Constrained Optimization <https://doi.org/10.1137/21M1460971>`_, *SIAM Journal on Optimization*, 21:4 (2022), pp. 2552-2579 [`preprint arXiv 2111.05443 <https://arxiv.org/abs/2111.05443>`_].
3. Y Liu, K H Lam and L Roberts, `Black-box Optimization Algorithms for Regularized Least-squares Problems <http://arxiv.org/abs/2407.14915>`_, *arXiv preprint arXiv:arXiv:2407.14915*, 2024.
If you use DFO-LS in a paper, please cite [1].
If your problem has constraints, including bound constraints, please cite [1,2].
If your problem includes a regularizer, please cite [1,3].
Requirements
------------
DFO-LS requires the following software to be installed:
* Python 3.9 or higher (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 (http://www.numpy.org/)
* SciPy version 1.11 or higher (http://www.scipy.org/)
* Pandas (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 conda
------------------------
DFO-LS can be directly installed in Anaconda environments using `conda-forge <https://anaconda.org/conda-forge/dfo-ls>`_:
.. code-block:: bash
$ conda install -c conda-forge dfo-ls
Installation using pip
----------------------
For easy installation, use `pip <http://www.pip-installer.org/>`_ as root:
.. code-block:: bash
$ pip install DFO-LS
Note that if an older install of DFO-LS is present on your system you can use:
.. code-block:: bash
$ 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
$ pip install .
To upgrade DFO-LS to the latest version, navigate to the top-level directory (i.e. the one containing :code:`pyproject.toml`) and rerun the installation using :code:`pip`, as above:
.. code-block:: bash
$ git pull
$ pip install .
Testing
-------
If you installed DFO-LS manually, you can test your installation using the pytest package:
.. code-block:: bash
$ pip install pytest
$ python -m pytest --pyargs dfols
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
$ 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 <https://github.com/numericalalgorithmsgroup/dfols/issues>`_.
License
-------
This algorithm is released under the GNU GPL license. Please `contact NAG <http://www.nag.com/content/worldwide-contact-information>`_ for alternative licensing.
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"description": "===================================================\nDFO-LS: Derivative-Free Optimizer for Least-Squares\n===================================================\n\n.. image:: https://github.com/numericalalgorithmsgroup/dfols/actions/workflows/python_testing.yml/badge.svg\n :target: https://github.com/numericalalgorithmsgroup/dfols/actions\n :alt: Build Status\n\n.. image:: https://img.shields.io/badge/License-GPL%20v3-blue.svg\n :target: https://www.gnu.org/licenses/gpl-3.0\n :alt: GNU GPL v3 License\n\n.. image:: https://img.shields.io/pypi/v/DFO-LS.svg\n :target: https://pypi.python.org/pypi/DFO-LS\n :alt: Latest PyPI version\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2630426.svg\n :target: https://doi.org/10.5281/zenodo.2630426\n :alt: DOI:10.5281/zenodo.2630426\n \n.. image:: https://static.pepy.tech/personalized-badge/dfo-ls?period=total&units=international_system&left_color=black&right_color=green&left_text=Downloads\n :target: https://pepy.tech/project/dfo-ls\n :alt: Total downloads\n\nDFO-LS is a flexible package for solving nonlinear least-squares minimization, without requiring derivatives of the objective. 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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 arXiv 1804.00154 <https://arxiv.org/abs/1804.00154>`_] . \n2. M Hough and L Roberts, `Model-Based Derivative-Free Methods for Convex-Constrained Optimization <https://doi.org/10.1137/21M1460971>`_, *SIAM Journal on Optimization*, 21:4 (2022), pp. 2552-2579 [`preprint arXiv 2111.05443 <https://arxiv.org/abs/2111.05443>`_].\n3. 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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`. 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