===================================================
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
.. image:: https://static.pepy.tech/personalized-badge/dfo-ls?period=total&units=international_system&left_color=black&right_color=green&left_text=Downloads
:target: https://pepy.tech/project/dfo-ls
:alt: Total downloads
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.
If you use DFO-LS for problems with constraints, including bound constraints, please also cite:
Hough, M. and Roberts, L., `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.
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
$ [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:`pyproject.toml`) 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 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
$ [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 <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.
Raw data
{
"_id": null,
"home_page": null,
"name": "DFO-LS",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "Lindon Roberts <lindon.roberts@sydney.edu.au>",
"keywords": "mathematics, optimization, least squares, derivative free optimization, nonlinear least squares",
"author": null,
"author_email": "Lindon Roberts <lindon.roberts@sydney.edu.au>",
"download_url": "https://files.pythonhosted.org/packages/0e/fe/e5aababbee593516a5b0ae3627aba692f3ad426c46ffb09a341137677dd9/DFO-LS-1.4.1.tar.gz",
"platform": null,
"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. 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>`_.\n\nThis 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. \n\nIf 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.\n\nDocumentation\n-------------\nSee manual.pdf or `here <https://numericalalgorithmsgroup.github.io/dfols/>`_.\n\nCitation\n--------\nIf you use DFO-LS in a paper, please cite:\n\nCartis, 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.\n\nIf you use DFO-LS for problems with constraints, including bound constraints, please also cite:\n\nHough, M. and Roberts, L., `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.\n\nRequirements\n------------\nDFO-LS requires the following software to be installed:\n\n* Python 3.9 or higher (http://www.python.org/)\n\nAdditionally, the following python packages should be installed (these will be installed automatically if using *pip*, see `Installation using pip`_):\n\n* NumPy (http://www.numpy.org/)\n* SciPy version 1.11 or higher (http://www.scipy.org/)\n* Pandas (http://pandas.pydata.org/)\n\n**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.\n\nInstallation using conda\n------------------------\nDFO-LS can be directly installed in Anaconda environments using `conda-forge <https://anaconda.org/conda-forge/dfo-ls>`_:\n\n.. code-block:: bash\n\n $ conda install -c conda-forge dfo-ls\n\nInstallation using pip\n----------------------\nFor easy installation, use `pip <http://www.pip-installer.org/>`_ as root:\n\n.. code-block:: bash\n\n $ [sudo] pip install DFO-LS\n\nor alternatively *easy_install*:\n\n.. code-block:: bash\n\n $ [sudo] easy_install DFO-LS\n\nIf you do not have root privileges or you want to install DFO-LS for your private use, you can use:\n\n.. code-block:: bash\n\n $ pip install --user DFO-LS\n\nwhich will install DFO-LS in your home directory.\n\nNote that if an older install of DFO-LS is present on your system you can use:\n\n.. code-block:: bash\n\n $ [sudo] pip install --upgrade DFO-LS\n\nto upgrade DFO-LS to the latest version.\n\nManual installation\n-------------------\nAlternatively, you can download the source code from `Github <https://github.com/numericalalgorithmsgroup/dfols>`_ and unpack as follows:\n\n .. code-block:: bash\n\n $ git clone https://github.com/numericalalgorithmsgroup/dfols\n $ cd dfols\n\nDFO-LS is written in pure Python and requires no compilation. It can be installed using:\n\n .. code-block:: bash\n\n $ [sudo] pip install .\n\nIf you do not have root privileges or you want to install DFO-LS for your private use, you can use:\n\n .. code-block:: bash\n\n $ pip install --user .\n\ninstead.\n\nTo 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:\n\n .. code-block:: bash\n\n $ git pull\n $ [sudo] pip install . # with admin privileges\n\nTesting\n-------\nIf you installed DFO-LS manually, you can test your installation using the pytest package:\n\n .. code-block:: bash\n\n $ pip install pytest\n $ python -m pytest --pyargs dfols\n\nAlternatively, the HTML documentation provides some simple examples of how to run DFO-LS.\n\nExamples\n--------\nExamples 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.\n\nUninstallation\n--------------\nIf DFO-LS was installed using *pip* you can uninstall as follows:\n\n .. code-block:: bash\n\n $ [sudo] pip uninstall DFO-LS\n\nIf DFO-LS was installed manually you have to remove the installed files by hand (located in your python site-packages directory).\n\nBugs\n----\nPlease report any bugs using `GitHub's issue tracker <https://github.com/numericalalgorithmsgroup/dfols/issues>`_.\n\nLicense\n-------\nThis algorithm is released under the GNU GPL license. Please `contact NAG <http://www.nag.com/content/worldwide-contact-information>`_ for alternative licensing.\n",
"bugtrack_url": null,
"license": "GPL-3.0-or-later",
"summary": "A flexible derivative-free solver for (bound constrained) nonlinear least-squares minimization",
"version": "1.4.1",
"project_urls": {
"Bug Tracker": "https://github.com/numericalalgorithmsgroup/dfols/issues/",
"Documentation": "https://numericalalgorithmsgroup.github.io/dfols/",
"Download": "https://github.com/numericalalgorithmsgroup/dfols/releases/",
"Homepage": "https://github.com/numericalalgorithmsgroup/dfols",
"Source Code": "https://github.com/numericalalgorithmsgroup/dfols"
},
"split_keywords": [
"mathematics",
" optimization",
" least squares",
" derivative free optimization",
" nonlinear least squares"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "992175ec9a246cfee2f26ab220c733e500cdc0e4eea98a071b40bc3050a1fa8e",
"md5": "9cfd98e77055f9178da4b42472c6490a",
"sha256": "0aa03ebdc042c25ab1c38d379e20ae3dcde5d14fbac569bc4b2c75d43976a78b"
},
"downloads": -1,
"filename": "DFO_LS-1.4.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "9cfd98e77055f9178da4b42472c6490a",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 61214,
"upload_time": "2024-04-11T06:25:57",
"upload_time_iso_8601": "2024-04-11T06:25:57.274080Z",
"url": "https://files.pythonhosted.org/packages/99/21/75ec9a246cfee2f26ab220c733e500cdc0e4eea98a071b40bc3050a1fa8e/DFO_LS-1.4.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0efee5aababbee593516a5b0ae3627aba692f3ad426c46ffb09a341137677dd9",
"md5": "3ddd613dca7e6f69bdf3048112803ef0",
"sha256": "a1724a9c4457ddbff8ab75ad690e35ac8abc2352ee6378ad4673cf572df9f866"
},
"downloads": -1,
"filename": "DFO-LS-1.4.1.tar.gz",
"has_sig": false,
"md5_digest": "3ddd613dca7e6f69bdf3048112803ef0",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9",
"size": 57287,
"upload_time": "2024-04-11T06:25:58",
"upload_time_iso_8601": "2024-04-11T06:25:58.964879Z",
"url": "https://files.pythonhosted.org/packages/0e/fe/e5aababbee593516a5b0ae3627aba692f3ad426c46ffb09a341137677dd9/DFO-LS-1.4.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-11 06:25:58",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "numericalalgorithmsgroup",
"github_project": "dfols",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "dfo-ls"
}
JSON
