Structural Optimization and Scientific Computing

optymus is a Python library for solving optimization problems in mechanical engineering and scientific computing. Built on JAX for automatic differentiation, it provides efficient gradient computation and GPU acceleration. The library is designed for structural optimization, topology optimization, and general-purpose numerical optimization.

1. Install optymus:

   uv add optymus

2. Basic optimization example:

   from optymus import Optimizer
   from optymus.benchmark import Mccormick
   
   import jax.numpy as jnp
   
   f = Mccormick()
   initial_point = jnp.array([2.0, 2.0])
   
   opt = Optimizer(f_obj=f,
                   x0=initial_point,
                   method='bfgs')
   
   opt.report()

3. Topology optimization with engineering domains:

   from optymus.benchmark import MbbDomain
   from optymus.methods import polymesher
   
   # MBB beam domain with boundary conditions
   domain = MbbDomain
   
   # Generate polygonal mesh
   result = polymesher(domain=domain, num_elements=100)

If you use optymus in your research, please cite:

@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}
}