lineax
Linear solvers in JAX and Equinox.
Decision gist · record as of 2026-08-14
Yes, if you work in JAX and need linear solves or least-squares. The library is actively maintained, has low install friction, and integrates seamlessly with JAX's autodiff and GPU/TPU support. The Apache 2.0 license poses no restrictions. Alpha status (Development Status 3) means the API may change, but the package is in active use and well-documented.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11+, JAX 0.4.38+, and Equinox 0.11.10+.
- Low install friction with a pure-Python wheel.
- Actively maintained as of August 2026 with recent releases.
License · maintenance · safety
permissive license (permissive) — Apache 2.0 permissive license allows commercial and derivative use with minimal restrictions—attribution and license notice required in distributions.
last release 2026-05-01 (105 days) · last repo commit 2026-08-14 · 572 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 523,152 downloads/mo, #6,197 on PyPI
Alternatives
Verify before relying
pip install lineax
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())- Whether structured matrix support (e.g. symmetric) provides measurable performance gains in typical workflows.
- Stability and numerical accuracy of gradients through least-squares compared to alternative autodiff approaches.
- Performance characteristics of implicit operators on GPU/TPU relative to explicit matrix solves.
What it is and what it does
Lineax is a JAX library for solving linear systems and least-squares problems, with support for both explicit matrices and implicit linear operators (e.g., Jacobians, transposes). It integrates with JAX's autodiff, autoparallelism, and GPU/TPU support, allowing you to differentiate through linear solves with numerically stable gradients. The library works with PyTree-valued matrices and vectors, and supports structured matrix types like symmetric matrices.
Typical use cases include solving Ax=b when A may be ill-posed or rectangular, computing least-squares solutions via QR or other solvers, and building implicit operators for problems where materializing the full matrix is infeasible or inefficient. It is part of the broader JAX ecosystem and depends on Equinox for PyTree utilities and jaxtyping for type annotations.
Use it for
- Solve overdetermined or underdetermined linear systems without materializing the full matrix operator.
- Compute least-squares solutions with numerically stable gradients for optimization and inverse problems.
- Build implicit Jacobian and Hessian operators for Newton-like methods in scientific computing.
- Solve quadratic minimization problems by constructing a Hessian linear operator and solving the normal equations.
- Integrate linear solves into JAX-based neural network training pipelines with automatic differentiation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work in JAX and need linear solves or least-squares.
The library is actively maintained, has low install friction, and integrates seamlessly with JAX's autodiff and GPU/TPU support. The Apache 2.0 license poses no restrictions. Alpha status (Development Status 3) means the API may change, but the package is in active use and well-documented.
Install
lineax on PyPI
Before you install
Low install friction with a pure-Python wheel. Actively maintained as of August 2026 with recent releases. Requires JAX 0.4.38+ and Equinox 0.11.10+, which are themselves substantial dependencies.
Requires Python 3.11+, JAX 0.4.38+, and Equinox 0.11.10+.
License in practice
Apache 2.0 permissive license allows commercial and derivative use with minimal restrictions—attribution and license notice required in distributions.
Quickstart
pip install lineax
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())Verify before relying
- Whether structured matrix support (e.g. symmetric) provides measurable performance gains in typical workflows.
- Stability and numerical accuracy of gradients through least-squares compared to alternative autodiff approaches.
- Performance characteristics of implicit operators on GPU/TPU relative to explicit matrix solves.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release ~=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesequinoxjaxjaxtypingtyping-extensions |
| Maintenance | Actively maintained 105 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 523,152 / month, #6,197 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: Financial and Insurance IndustryIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Information AnalysisTopic :: Scientific/Engineering :: Mathematics |
Evidence: lineax-0.1.1-py3-none-any.whl
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