brutax 0.0.2

Function minimization by brute force grid search in JAX

Brutax

When all else fails, why not a brute-force search! Brutax is a JAX library for function optimization by brute-force grid search. Features include:

• Highly-parallel function evaluations
• PyTree-valued grids
• Customizable search behavior downstream
• Smooth integration with JAX function transformations: JIT, autodiff, vectorization, and scaling across GPU/TPUs

Installation

pip install brutax

Quick example

import brutax
import jax.numpy as jnp

def fn(tree_grid_point, _):
    x, y = tree_grid_point
    return (x - 1.)**2 + (y - 2.)**2

# The `tree_grid` is the cartesian product of x and y grids
x_grid, y_grid = (jnp.arange(-5., 5., 1), jnp.arange(-5., 5., 0.1))
tree_grid = (x_grid, y_grid)
# Run grid search over (10 x 100) grid
sol = brutax.run_grid_search(
    fn, method=brutax.MinimumSearchMethod(), tree_grid=tree_grid, args=None
)
x_min, y_min = sol.value
assert jnp.isclose(x_min, 1.) and jnp.isclose(y_min, 2.)

Acknowledgements

• The design of brutax is heavily inspired from the JAX non-linear optimization library optimistix.