optymus 0.1.11

Optimization methods in Python

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.

Key Features

• Topology Optimization: PolyMesher-based mesh generation with signed distance functions
• Pre-built Engineering Domains: Cook membrane, Michell truss, MBB beam, Wrench, Suspension
• Structural Analysis Support: Boundary conditions, load definitions, and FEM-ready domains
• Multiple Optimization Methods: Gradient-based (BFGS, Conjugate Gradient, Newton), adaptive (Adam, AdaGrad), and evolutionary (CMA-ES, Differential Evolution, Particle Swarm)
• Constraint Handling: Support for equality and inequality constraints via penalty methods

Getting Started

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)
   

Documentation

Visit the official documentation for:

• Complete API reference
• Optimization method guides
• Mechanical engineering examples
• Topology optimization tutorials

Citation

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}
}