Benchmarkfcns is an effort to provide a high-perfomant, public and free implementation of well-known benchmark functions for mathematical optimization algorithms in Python. The Python library is implemented in C++ and utilizes the powerful SIMD vector calucluations offered by the Eigen library.

For the documentation of the implemented functions and their features, please visit https://benchmarkfcns.info.

The library is packaged and available on the PyPI index. To install, simply run pip install benchmarkfcns.

The library is built with with pybind11, scikit-build-core and Eigen. To compile the library, you will need to have CMake version 3.15 or above installed and configured. The easiest way to compile and install the package locally is to clone the repository, e.g. BenchmarkFcns, and then run pip install ./BenchmarkFcns. Although optional, it is highly recommended to install the package into a virtual environment.

Any suggestions and contributions are welcomed. The best way to contribute to the library is to fork the repository, apply the contributions and create a pull request.

To install and use the MATLAB implementation, it is just required to add the project folders to MATLAB's path. For example, to use the functions in the 'benchmarks/MATLAB' folder, just navigate to this folder with MATLAB's directory explorer or use the command addpath with path to the folder on your PC (e.g. addpath /path/to/benchmarks).

Any bug reports, code contributions, suggestions, feedback and insights are greatly appreciated. If you are using this library in a research publication, please kindly consider citing the repository as "Ardeh, M. A. [2024] BenchmarkFcns [Source Code]. GitHub. https://github.com/mazhar-ansari-ardeh/BenchmarkFcns".