linrax 2025.2.1

A JAX-compatible, simplex method-based linear program solver

linrax

JAX-compatible, simplex method-based linear program solver. As part of the JAX ecosystem, linrax supports

• JIT compilation,
• Automatic Differentiability (forward mode only, currently), and
• GPU parallelization.

linrax is designed for use as a subroutine in a larger JAX pipeline. Its performance excels on smaller problems ($<50$ input variables), and is fully tracable in any of JAX's main transformations. In particular, linrax can solve problems that are specified with linearly dependent constraints, an area where other JAX-based solvers struggle.

Usage

The interface of linrax is designed to closely mimic that of scipy.linprog. The public function is

import jax 
import jax.numpy as jnp
@partial(jax.jit, static_argnames=[ "unbounded"])
def linprog(
    c: jax.Array,
    A_ub: jax.Array = jnp.empty((0, 0)),
    b_ub: jax.Array = jnp.empty((0,)),
    A_eq: jax.Array = jnp.empty((0, 0)),
    b_eq: jax.Array = jnp.empty((0,)),
    unbounded: bool = False,
) -> Tuple[SimplexStep, SimplexSolutionType]:
    ...

The SimplexSolutionType contains fields indicating if the problem is feasible or bounded, and a success property to check both simultaneously. Assuming the problem has solutions, the SimplexStep object describes this solution. In particular, the fields x and fun retrieve the optimal point and objective value, respectively.