This library provides a comprehensive collection of optimization methods, both with and without constraints. The main goal is provide a simple structure to improve research and development in optimization problems.

To begin using optymus, follow these steps:

1. Install optymus:

   pip install optymus

2. Get Started:

   from optymus import Optimizer
   from optymus.benchmark import MccormickFunction
   
   import jax.numpy as jnp
   
   f = MccormickFunction()
   initial_point = np.array([2, 2])
   
   opt = Optimizer(f_obj=f,
                   x0=initial_point,
                   method='bfgs')
   
   opt.print_report()

3. Explore the Documentation: Visit the official documentation to understand the available optimization methods and how to use them effectively.

Refer to the documentation for detailed information on each method and its application.

Contributions to Optymus are highly appreciated. If you have additional optimization methods, improvements, or bug fixes, please submit a pull request following the contribution guidelines.

If you use optymus in your research, please consider citing the library using the following BibTeX entry:

@misc{optymus2024,
  author = {da Costa, Kleyton and Menezes, Ivan and Lopes, Helio},
  title = {Optymus: Optimization Methods Library for Python},
  year = {2024},
  note = {GitHub Repository},
  url = {https://github.com/quant-sci/optymus}
}

optymus is part of quantsci project.