COBYQA documentation#
- Version:
1.1
- Useful links:
- Authors:
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
where \(\mathcal{X} = \{ x \in \mathbb{R}^n : l \le x \le u \}\). COBYQA always respects the bound constraints throughout the optimization process. Hence, the nonlinear functions \(f\) and \(c\) do not need to be well-defined outside \(\mathcal{X}\). 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 framework description.
To install COBYQA using pip
, run in your terminal
pip install cobyqa
If you are using conda
, you can install COBYQA from the conda-forge channel by running
conda install conda-forge::cobyqa
For more details on the installation and the usage of COBYQA, see the 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 1.1.2. 2024. URL: https://www.cobyqa.com.
The corresponding BibTeX entries are given hereunder.
@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 1.1.2},
year = 2024,
url = {https://www.cobyqa.com},
}
Statistics#
As of October 28, 2024, COBYQA has been downloaded 11,782 times, including
9,672 times on PyPI (mirror downloads excluded), and
2,110 times on conda-forge.
The following figure shows the cumulative downloads of COBYQA.
(Source code
, png
, hires.png
, pdf
)
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.