Optimization Methods

This module provides a comprehensive collection of optimization algorithms for solving mathematical optimization problems. The methods are organized into seven categories based on the type of information they use (derivative-free, gradient-based, Hessian-based) and their algorithmic approach.

Zero-Order Methods

Derivative-free optimization methods that only require function evaluations.

optymus.methods.powell(**kwargs)
optymus.methods.univariate(**kwargs)

First-Order Methods

Gradient-based optimization methods that utilize first-order derivative information.

optymus.methods.bfgs(**kwargs)
optymus.methods.conjugate_gradient([...])
optymus.methods.steepest_descent(**kwargs)

Second-Order Methods

Methods that utilize second-order derivative (Hessian) information.

optymus.methods.newton_raphson([htype])

Adaptive Learning Rate Methods

Gradient-based methods with adaptive step-size mechanisms, commonly used in deep learning.

optymus.methods.adagrad(**kwargs)
optymus.methods.adam(**kwargs)
optymus.methods.adamax(**kwargs)
optymus.methods.rmsprop(**kwargs)
optymus.methods.yogi(**kwargs)

Stochastic Methods

Probabilistic optimization algorithms that use randomness to explore the search space.

optymus.methods.cmaes([bounds, pop_size, sigma])	CMA-ES (Covariance Matrix Adaptation Evolution Strategy) optimization algorithm.
optymus.methods.cross_entropy([bounds, ...])	Cross-Entropy Method optimization algorithm.
optymus.methods.simulated_annealing([...])	Simulated Annealing optimization algorithm.

Population-Based Methods

Evolutionary and swarm intelligence algorithms that maintain a population of candidate solutions.

optymus.methods.particle_swarm([divergence, ...])	Particle Swarm Optimization algorithm.
optymus.methods.differential_evolution([...])	Particle Swarm Optimization algorithm.

Topological Optimization Methods

Specialized methods for topology optimization problems.

optymus.methods.polymesher(domain, ...[, ...])

PolyMesher