{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Lineax solves linear systems and least-squares problems in JAX, handling both explicit matrices and implicit linear operators without materializing them.","skillfed_tags":["jax-ecosystem","numerical-methods","autodiff"],"use_cases":["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."],"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.\n\nTypical 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.","worth_installing":"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."},"id":"lineax","links":{"html":"https://skillfed.io/packages/lineax","md":"https://skillfed.io/packages/lineax.md","pypi":"https://pypi.org/project/lineax/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-01","license_spdx":null,"license_treatment":"permissive","name":"lineax","python_support":"supports_current","summary":"Linear solvers in JAX and Equinox."},"popularity":{"monthly_downloads":523152,"position":6197,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.1"}