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optymus is a Python library designed to address optimization problems in science and engineering. Built onJAX, allowing automatic differentiation for efficient computation of gradients and Hessians. The library emphasizes ease of use and flexibility, enabling users to solve optimization problems with minimal setup. optymus also provides robust capabilities for visualization and benchmarking, allowing users to gain insights into method behavior and compare performance effectively.

To begin usingoptymus, follow these steps:

1. Install optymus:

   pip install optymus --upgrade# update versionpip install optymus[all]# all dependenciespip install optymus[methods]# without plot dependencies

2. Get Started:

   fromoptymusimportOptimizerfromoptymus.benchmarkimportMccormickFunctionimportjax.numpyasjnpf=MccormickFunction()initial_point=jnp.array([2,2])opt=Optimizer(f_obj=f,x0=initial_point,method='bfgs')opt.report()

3. Explore the Documentation:Visit theofficial 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 thecontribution guidelines.

If you useoptymus 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 in Python},year ={2024},note ={GitHub Repository},url ={https://github.com/quant-sci/optymus}}

optymus is part ofquantsci project.