Getting started¤

Lineax is a JAX library for linear solves and linear least squares. That is, Lineax provides routines that solve for \(x\) in \(Ax = b\). (Even when \(A\) may be ill-posed or rectangular.)

Features include:

PyTree-valued matrices and vectors;
General linear operators for Jacobians, transposes, etc.;
Efficient linear least squares (e.g. QR solvers);
Numerically stable gradients through linear least squares;
Support for structured (e.g. symmetric) matrices;
Improved compilation times;
Improved runtime of some algorithms;
Support for both real-valued and complex-valued inputs;
All the benefits of working with JAX: autodiff, autoparallism, GPU/TPU support, etc.

Installation¤

pip install lineax

Requires Python 3.9+, JAX 0.4.13+, and Equinox 0.11.0+.

Quick example¤

Lineax can solve a least squares problem with an explicit matrix operator:

import jax.random as jr
import lineax as lx

matrix_key, vector_key = jr.split(jr.PRNGKey(0))
matrix = jr.normal(matrix_key, (10, 8))
vector = jr.normal(vector_key, (10,))
operator = lx.MatrixLinearOperator(matrix)
solution = lx.linear_solve(operator, vector, solver=lx.QR())

or Lineax can solve a problem without ever materializing a matrix, as done in this quadratic solve:

import jax
import lineax as lx

key = jax.random.PRNGKey(0)
y = jax.random.normal(key, (10,))

def quadratic_fn(y, args):
  return jax.numpy.sum((y - 1)**2)

gradient_fn = jax.grad(quadratic_fn)
hessian = lx.JacobianLinearOperator(gradient_fn, y, tags=lx.positive_semidefinite_tag)
solver = lx.CG(rtol=1e-6, atol=1e-6)
out = lx.linear_solve(hessian, gradient_fn(y, args=None), solver)
minimum = y - out.value

Next steps¤

Check out the examples or the API reference on the left-hand bar.

Getting started¤

Installation¤

Quick example¤

Next steps¤

See also: other libraries in the JAX ecosystem¤