====================================================================
Py-BOBYQA: Derivative-Free Solver for Bound-Constrained Minimization
====================================================================
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Py-BOBYQA is a flexible package for solving bound-constrained general objective minimization, without requiring derivatives of the objective. At its core, it is a Python implementation of the BOBYQA algorithm by Powell, but Py-BOBYQA has extra features improving its performance on some problems (see the papers below for details). Py-BOBYQA is particularly useful when evaluations of the objective function are expensive and/or noisy.
More details about Py-BOBYQA and its enhancements over BOBYQA can be found in our papers:
1. Coralia Cartis, Jan Fiala, Benjamin Marteau and Lindon 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 [`arXiv preprint: 1804.00154 <https://arxiv.org/abs/1804.00154>`_]
2. Coralia Cartis, Lindon Roberts and Oliver Sheridan-Methven, `Escaping local minima with derivative-free methods: a numerical investigation <https://doi.org/10.1080/02331934.2021.1883015>`_, *Optimization*, 71:8 (2022), pp. 2343-2373. [`arXiv preprint: 1812.11343 <https://arxiv.org/abs/1812.11343>`_]
3. Lindon Roberts, `Model Construction for Convex-Constrained Derivative-Free Optimization <https://arxiv.org/abs/2403.14960>`_, *arXiv preprint arXiv:2403.14960* (2024).
Please cite [1] when using Py-BOBYQA for local optimization, [1,2] when using Py-BOBYQA's global optimization heuristic functionality, and [1,3] if using the general convex constraints functionality.
The original paper by Powell is: M. J. D. Powell, The BOBYQA algorithm for bound constrained optimization without derivatives, technical report DAMTP 2009/NA06, University of Cambridge (2009), and the original Fortran implementation is available `here <http://mat.uc.pt/~zhang/software.html>`_.
If you are interested in solving least-squares minimization problems, you may wish to try `DFO-LS <https://github.com/numericalalgorithmsgroup/dfols>`_, which has the same features as Py-BOBYQA (plus some more), and exploits the least-squares problem structure, so performs better on such problems.
Documentation
-------------
See manual.pdf or the `online manual <https://numericalalgorithmsgroup.github.io/pybobyqa/>`_.
Citation
--------
Full details of the Py-BOBYQA algorithm are given in our papers:
1. Coralia Cartis, Jan Fiala, Benjamin Marteau and Lindon 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>`_]
2. Coralia Cartis, Lindon Roberts and Oliver Sheridan-Methven, `Escaping local minima with derivative-free methods: a numerical investigation <https://doi.org/10.1080/02331934.2021.1883015>`_, *Optimization*, 71:8 (2022), pp. 2343-2373. [`arXiv preprint: 1812.11343 <https://arxiv.org/abs/1812.11343>`_]
3. Lindon Roberts, `Model Construction for Convex-Constrained Derivative-Free Optimization <https://arxiv.org/abs/2403.14960>`_, *arXiv preprint arXiv:2403.14960* (2024).
Please cite [1] when using Py-BOBYQA for local optimization, [1,2] when using Py-BOBYQA's global optimization heuristic functionality, and [1,3] if using the general convex constraints functionality.
Requirements
------------
Py-BOBYQA requires the following software to be installed:
* Python 3.8 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 (http://www.scipy.org/)
* Pandas (http://pandas.pydata.org/)
**Optional package:** Py-BOBYQA 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 Py-BOBYQA to work, and it is not installed by default.
Installation using pip
----------------------
For easy installation, use `pip <http://www.pip-installer.org/>`_:
.. code-block:: bash
$ pip install Py-BOBYQA
Note that if an older install of Py-BOBYQA is present on your system you can use:
.. code-block:: bash
$ pip install --upgrade Py-BOBYQA
to upgrade Py-BOBYQA to the latest version.
Manual installation
-------------------
Alternatively, you can download the source code from `Github <https://github.com/numericalalgorithmsgroup/pybobyqa>`_ and unpack as follows:
.. code-block:: bash
$ git clone https://github.com/numericalalgorithmsgroup/pybobyqa
$ cd pybobyqa
Py-BOBYQA is written in pure Python and requires no compilation. It can be installed using:
.. code-block:: bash
$ pip install .
instead.
To upgrade Py-BOBYQA 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
$ pip install .
Testing
-------
If you installed Py-BOBYQA manually, you can test your installation using the pytest package:
.. code-block:: bash
$ pip install pytest
$ python -m pytest --pyargs pybobyqa
Alternatively, the HTML documentation provides some simple examples of how to run Py-BOBYQA.
Examples
--------
Examples of how to run Py-BOBYQA are given in the `online documentation <https://numericalalgorithmsgroup.github.io/pybobyqa/>`_, and the `examples directory <https://github.com/numericalalgorithmsgroup/pybobyqa/tree/master/examples>`_ in Github.
Uninstallation
--------------
If Py-BOBYQA was installed using *pip* you can uninstall as follows:
.. code-block:: bash
$ pip uninstall Py-BOBYQA
If Py-BOBYQA 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.
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"description": "====================================================================\nPy-BOBYQA: Derivative-Free Solver for Bound-Constrained Minimization\n====================================================================\n\n.. image:: https://github.com/numericalalgorithmsgroup/pybobyqa/actions/workflows/python_testing.yml/badge.svg\n :target: https://github.com/numericalalgorithmsgroup/pybobyqa/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/Py-BOBYQA.svg\n :target: https://pypi.python.org/pypi/Py-BOBYQA\n :alt: Latest PyPI version\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2630437.svg\n :target: https://doi.org/10.5281/zenodo.2630437\n :alt: DOI:10.5281/zenodo.2630437\n\n.. image:: https://static.pepy.tech/personalized-badge/py-bobyqa?period=total&units=international_system&left_color=black&right_color=green&left_text=Downloads\n :target: https://pepy.tech/project/py-bobyqa\n :alt: Total downloads\n\nPy-BOBYQA is a flexible package for solving bound-constrained general objective minimization, without requiring derivatives of the objective. At its core, it is a Python implementation of the BOBYQA algorithm by Powell, but Py-BOBYQA has extra features improving its performance on some problems (see the papers below for details). Py-BOBYQA is particularly useful when evaluations of the objective function are expensive and/or noisy.\n\nMore details about Py-BOBYQA and its enhancements over BOBYQA can be found in our papers:\n\n1. Coralia Cartis, Jan Fiala, Benjamin Marteau and Lindon 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 [`arXiv preprint: 1804.00154 <https://arxiv.org/abs/1804.00154>`_] \n2. Coralia Cartis, Lindon Roberts and Oliver Sheridan-Methven, `Escaping local minima with derivative-free methods: a numerical investigation <https://doi.org/10.1080/02331934.2021.1883015>`_, *Optimization*, 71:8 (2022), pp. 2343-2373. [`arXiv preprint: 1812.11343 <https://arxiv.org/abs/1812.11343>`_] \n3. Lindon Roberts, `Model Construction for Convex-Constrained Derivative-Free Optimization <https://arxiv.org/abs/2403.14960>`_, *arXiv preprint arXiv:2403.14960* (2024).\n\nPlease cite [1] when using Py-BOBYQA for local optimization, [1,2] when using Py-BOBYQA's global optimization heuristic functionality, and [1,3] if using the general convex constraints functionality.\n\nThe original paper by Powell is: M. J. D. Powell, The BOBYQA algorithm for bound constrained optimization without derivatives, technical report DAMTP 2009/NA06, University of Cambridge (2009), and the original Fortran implementation is available `here <http://mat.uc.pt/~zhang/software.html>`_.\n\nIf you are interested in solving least-squares minimization problems, you may wish to try `DFO-LS <https://github.com/numericalalgorithmsgroup/dfols>`_, which has the same features as Py-BOBYQA (plus some more), and exploits the least-squares problem structure, so performs better on such problems.\n\nDocumentation\n-------------\nSee manual.pdf or the `online manual <https://numericalalgorithmsgroup.github.io/pybobyqa/>`_.\n\nCitation\n--------\nFull details of the Py-BOBYQA algorithm are given in our papers: \n\n1. Coralia Cartis, Jan Fiala, Benjamin Marteau and Lindon 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>`_] \n2. Coralia Cartis, Lindon Roberts and Oliver Sheridan-Methven, `Escaping local minima with derivative-free methods: a numerical investigation <https://doi.org/10.1080/02331934.2021.1883015>`_, *Optimization*, 71:8 (2022), pp. 2343-2373. [`arXiv preprint: 1812.11343 <https://arxiv.org/abs/1812.11343>`_]\n3. Lindon Roberts, `Model Construction for Convex-Constrained Derivative-Free Optimization <https://arxiv.org/abs/2403.14960>`_, *arXiv preprint arXiv:2403.14960* (2024).\n\nPlease cite [1] when using Py-BOBYQA for local optimization, [1,2] when using Py-BOBYQA's global optimization heuristic functionality, and [1,3] if using the general convex constraints functionality.\n\nRequirements\n------------\nPy-BOBYQA requires the following software to be installed:\n\n* Python 3.8 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 (http://www.scipy.org/)\n* Pandas (http://pandas.pydata.org/)\n\n**Optional package:** Py-BOBYQA 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 Py-BOBYQA to work, and it is not installed by default.\n\nInstallation using pip\n----------------------\nFor easy installation, use `pip <http://www.pip-installer.org/>`_:\n\n .. code-block:: bash\n\n $ pip install Py-BOBYQA\n\nNote that if an older install of Py-BOBYQA is present on your system you can use:\n\n .. code-block:: bash\n\n $ pip install --upgrade Py-BOBYQA\n\nto upgrade Py-BOBYQA to the latest version.\n\nManual installation\n-------------------\nAlternatively, you can download the source code from `Github <https://github.com/numericalalgorithmsgroup/pybobyqa>`_ and unpack as follows:\n\n .. code-block:: bash\n\n $ git clone https://github.com/numericalalgorithmsgroup/pybobyqa\n $ cd pybobyqa\n\nPy-BOBYQA is written in pure Python and requires no compilation. 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