MPAX: Mathematical Programming in JAX

Code

• github.com/mit-lu-lab/mpax
  OfficialIn paperjax★ 148

Abstract

This paper presents MPAX (Mathematical Programming in JAX), a versatile and efficient toolbox for integrating linear programming (LP) into machine learning workflows. MPAX implemented the state-of-the-art first-order methods, restarted average primal-dual hybrid gradient and reflected restarted Halpern primal-dual hybrid gradient, to solve LPs in JAX. This provides native support for hardware accelerations along with features like batch solving, auto-differentiation, and device parallelism. Extensive numerical experiments demonstrate the advantages of MPAX over existing solvers. The solver is available at https://github.com/MIT-Lu-Lab/MPAX.

Reproductions