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Lineax: unified linear solves and linear least-squares in JAX and Equinox

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arxiv 2311.17283 v1 pith:2H5H6H4U submitted 2023-11-28 cs.MS

Jason Rader , Terry Lyons , Patrick Kidger This is my paper · ORCID
classification cs.MS
keywords linearlineaxleast-squaressolvesequinoxoperatorsautodifferentiableavailable
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce Lineax, a library bringing linear solves and linear least-squares to the JAX+Equinox scientific computing ecosystem. Lineax uses general linear operators, and unifies linear solves and least-squares into a single, autodifferentiable API. Solvers and operators are user-extensible, without requiring the user to implement any custom derivative rules to get differentiability. Lineax is available at https://github.com/google/lineax.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. JAX-AMG: A GPU-Accelerated Differentiable Sparse Linear Solver Library for JAX

    cs.MS 2026-06 unverdicted novelty 7.0 of 10

    JAX-AMG is a new library that exposes AmgX AMG and Krylov methods as JAX primitives supporting JIT, reverse-mode AD, batched solves, and distributed execution.

  2. Complex surface patterning in homo- and heteroepitaxial contexts: (simultaneous) step bunching and step meandering

    cond-mat.mtrl-sci 2026-04 unverdicted novelty 6.5 of 10

    A linked discrete-continuum model demonstrates that step bunching and meandering can coexist as common growth modes on vicinal surfaces, yielding diverse patterns beyond separate limiting cases.

  3. jaxdae: A JAX-native Differentiable Solver for Differential-Algebraic Equations in Coupled Multi-physics

    cs.MS 2026-07 conditional novelty 6.0 of 10

    jaxdae provides the first JAX-native differentiable DAE solver, using a frozen-grid BDF-2 replay adjoint for reverse-mode gradients and XLA-fused batched sweeps.

  4. Structure-Preserving Discretization and Model Reduction for Energy-Based Models

    math.NA 2025-07 conditional novelty 5.0 of 10

    A Petrov-Galerkin discretization framework preserves discrete dissipation inequalities for a general class of energy-based models, including circuits, Cahn-Hilliard, and doubly nonlinear diffusion.

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