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DFO-LS-1.3.0
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مشاهده بیشتر
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مشاهده بیشترتوضیحات
A flexible derivative-free solver for (bound constrained) nonlinear least-squares minimization
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | DFO-LS-1.3.0 |
| نام | DFO-LS |
| نسخه کتابخانه | 1.3.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Lindon Roberts |
| ایمیل نویسنده | lindon.roberts@anu.edu.au |
| آدرس صفحه اصلی | https://github.com/numericalalgorithmsgroup/dfols/ |
| آدرس اینترنتی | https://pypi.org/project/DFO-LS/ |
| مجوز | GNU GPL |
===================================================
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.
نحوه نصب
نصب پکیج whl DFO-LS-1.3.0:
pip install DFO-LS-1.3.0.whl
نصب پکیج tar.gz DFO-LS-1.3.0:
pip install DFO-LS-1.3.0.tar.gz