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, ACM Transactions on Mathematical Software, 45:3 (2019), pp. 32:1-32:41 [arXiv preprint: 1804.00154]
- 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]
- 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.
See manual.pdf or the online manual.
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, ACM Transactions on Mathematical Software, 45:3 (2019), pp. 32:1-32:41 [preprint]
- 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]
- 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.
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 pip install trustregion. You do not have to have trustregion installed for Py-BOBYQA to work, and it is not installed by default.
For easy installation, use pip:
$ pip install Py-BOBYQA
Note that if an older install of Py-BOBYQA is present on your system you can use:
$ pip install --upgrade Py-BOBYQA
to upgrade Py-BOBYQA to the latest version.
Alternatively, you can download the source code from Github and unpack as follows:
$ 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:
$ pip install .
instead.
To upgrade Py-BOBYQA to the latest version, navigate to the top-level directory (i.e. the one containing setup.py) and rerun the installation using pip, as above:
$ git pull $ pip install .
If you installed Py-BOBYQA manually, you can test your installation using the pytest package:
$ pip install pytest $ python -m pytest --pyargs pybobyqa
Alternatively, the HTML documentation provides some simple examples of how to run Py-BOBYQA.
Examples of how to run Py-BOBYQA are given in the online documentation, and the examples directory in Github.
If Py-BOBYQA was installed using pip you can uninstall as follows:
$ 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).
Please report any bugs using GitHub's issue tracker.
This algorithm is released under the GNU GPL license. Please contact NAG for alternative licensing.