==================================================================== 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:

• 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>]
• 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>]
• 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:

• 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>]
• 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>]
• 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

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This algorithm is released under the GNU GPL license. Please contact NAG <http://www.nag.com/content/worldwide-contact-information>_ for alternative licensing.