optymus documentation

Optimization and Scientific Computing

optymus is a Python library for solving optimization problems in engineering and scientific computing. Built on JAX for automatic differentiation, it provides efficient gradient computation and GPU acceleration. The library supports continuous optimization, topology optimization, and combinatorial optimization (vehicle routing).

Getting Started

1. Install the package:

uv add optymus

2. Create an optimization problem:

from optymus import Optimizer
from optymus.benchmark import Mccormick

import jax.numpy as jnp

f = Mccormick()
initial_point = jnp.array([2, 2])

3. Optimize the problem:

opt = Optimizer(f_obj=f,
                x0=initial_point,
                method='bfgs')

4. Print the optimization report:

opt.report()

Applications

optymus includes specialized support for:

• Structural compliance minimization

• Topology optimization with PolyMesher

• Shape optimization using signed distance functions

• Energy minimization in mechanical systems

• Vehicle Routing Problem (VRP) with Clarke-Wright savings and local search

See the Examples for detailed tutorials.

Citation

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

@software{optymus2025,
  author = {da Costa, Kleyton and Menezes, Ivan and Lopes, Helio},
  title = {Optymus: Optimization Methods in Python},
  year = {2025},
  url = {https://github.com/quant-sci/optymus},
  note = {Python library for optimization built on JAX}
}