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Contributing to optimization-benchmarks

Thank you for your interest in contributing! We welcome contributions of all kinds.

Quick Start

  1. Fork and clone the repository
git clone https://github.com/ak-rahul/optimization-benchmarks.git
cd optimization-benchmarks
  1. Install in development mode
pip install -e ".[dev]"
  1. Create a branch
git checkout -b feature/your-feature-name

Adding a New Function

Add your function to optimization_benchmarks/functions.py:

def your_function(x: np.ndarray) -> float:
"""
Your Function Name.
Domain: |x_i| ≤ 10.
Dimension: n.
Global minimum: f(x*) = value at x = location.

References:
 Author, Title, Year.
"""
x = np.asarray(x, dtype=float)
# Your implementation
return result

Add it to __init__.py exports and write tests in tests/test_functions.py.

Testing

Code Style

  • Run black optimization_benchmarks/ before committing
  • Use type hints
  • Follow NumPy docstring style

Pull Request Process

  1. Ensure all tests pass
  2. Update documentation if needed
  3. Push your branch and create a PR
  4. Respond to review comments

Questions?

Open an issue or contact the maintainers.

License

By contributing, you agree your contributions will be licensed under the MIT License.

Thank you for making optimization-benchmarks better! 🚀