==================================================================== Py-BOBYQA: Derivative-Free Solver for Bound-Constrained Minimization ==================================================================== .. image:: https://github.com/numericalalgorithmsgroup/pybobyqa/actions/workflows/python_testing.yml/badge.svg :target: https://github.com/numericalalgorithmsgroup/pybobyqa/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/Py-BOBYQA.svg :target: https://pypi.python.org/pypi/Py-BOBYQA :alt: Latest PyPI version .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2630437.svg :target: https://doi.org/10.5281/zenodo.2630437 :alt: DOI:10.5281/zenodo.2630437 .. image:: https://static.pepy.tech/personalized-badge/py-bobyqa?period=total&units=international_system&left_color=black&right_color=green&left_text=Downloads :target: https://pepy.tech/project/py-bobyqa :alt: Total downloads 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 `_, *ACM Transactions on Mathematical Software*, 45:3 (2019), pp. 32:1-32:41 [`arXiv preprint: 1804.00154 `_] 2. Coralia Cartis, Lindon Roberts and Oliver Sheridan-Methven, `Escaping local minima with derivative-free methods: a numerical investigation `_, *Optimization*, 71:8 (2022), pp. 2343-2373. [`arXiv preprint: 1812.11343 `_] 3. Lindon Roberts, `Model Construction for Convex-Constrained Derivative-Free Optimization `_, *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 `_. If you are interested in solving least-squares minimization problems, you may wish to try `DFO-LS `_, 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 `_. 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 `_, *ACM Transactions on Mathematical Software*, 45:3 (2019), pp. 32:1-32:41 [`preprint `_] 2. Coralia Cartis, Lindon Roberts and Oliver Sheridan-Methven, `Escaping local minima with derivative-free methods: a numerical investigation `_, *Optimization*, 71:8 (2022), pp. 2343-2373. [`arXiv preprint: 1812.11343 `_] 3. Lindon Roberts, `Model Construction for Convex-Constrained Derivative-Free Optimization `_, *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 `_ package for fast trust-region subproblem solutions. To install this, make sure you have a Fortran compiler (e.g. `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 `_: .. 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 `_ 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 `_, and the `examples directory `_ 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 `_ for alternative licensing.

Py-BOBYQA1.5.0

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