COBYQA documentation
====================
.. toctree::
:maxdepth: 1
:hidden:
User guide
API reference [
Developer guide
:Version: |version|
:Useful links: `Issue tracker `_ | `Mailing list `_
:Authors: `Tom M. Ragonneau `_ | `Zaikun Zhang `_
COBYQA is a derivative-free optimization solver designed to supersede `COBYLA `_.
Using only functions values, and no derivatives, it aims at solving problems of the form
.. math::
\min_{x \in \mathcal{X}} \quad f ( x ) \quad \text{s.t.} \quad
\begin{cases}
b_l \le A x \le b_u,\\
c_l \le c ( x ) \le c_u,
\end{cases}
where :math:`\mathcal{X} = \{ x \in \mathbb{R}^n : l \le x \le u \}`.
COBYQA always respect the bound constraints throughout the optimization process.
Hence, the nonlinear functions :math:`f` and :math:`c` do not need to be well-defined outside :math:`\mathcal{X}`.
In essence, COBYQA is a derivative-free trust-region SQP method based on quadratic models obtained by underdetermined interpolation.
For a more detailed description of the algorithm, see the :ref:`framework description `.
To install COBYQA using ``pip``, run in your terminal
.. code-block:: bash
pip install cobyqa
If you are using ``conda``, you can install COBYQA from the `conda-forge `_ channel by running
.. code-block:: bash
conda install conda-forge::cobyqa
For more details on the installation and the usage of COBYQA, see the :ref:`user guide `.
Citing COBYQA
-------------
If you would like to acknowledge the significance of COBYQA in your research, we suggest citing the project as follows.
- T.\ M.\ Ragonneau. "Model-Based Derivative-Free Optimization Methods and Software." PhD thesis. Hong Kong, China: Department of Applied Mathematics, The Hong Kong Polytechnic University, 2022. URL: https://theses.lib.polyu.edu.hk/handle/200/12294.
- T.\ M.\ Ragonneau and Z.\ Zhang. COBYQA Version |release|. |year|. URL: https://www.cobyqa.com.
The corresponding BibTeX entries are given hereunder.
.. code-block:: bib
:substitutions:
@phdthesis{rago_thesis,
title = {Model-Based Derivative-Free Optimization Methods and Software},
author = {Ragonneau, T. M.},
school = {Department of Applied Mathematics, The Hong Kong Polytechnic University},
address = {Hong Kong, China},
year = 2022,
url = {https://theses.lib.polyu.edu.hk/handle/200/12294},
}
@misc{razh_cobyqa,
author = {Ragonneau, T. M. and Zhang, Z.},
title = {{COBYQA} {V}ersion |release|},
year = |year|,
url = {https://www.cobyqa.com},
}
Statistics
----------
As of |today|, COBYQA has been downloaded |total_downloads| times, including
- |pypi_downloads| times on `PyPI `_ (`mirror downloads `_ excluded), and
- |conda_downloads| times on `conda-forge `_.
Acknowledgments
---------------
This work was partially supported by the `Research Grants Council `_ of Hong Kong under Grants PF18-24698, PolyU 253012/17P, PolyU 153054/20P, PolyU 153066/21P, and `The Hong Kong Polytechnic University `_.
]