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Project description
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.
Getting Started
To begin using optymus
, follow these steps:
-
Install optymus:
pip install optymus
-
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()
-
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
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.
How to cite
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.
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