{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"jaxlie implements Lie groups (SO2, SE2, SO3, SE3) for rigid body transformations in JAX, providing differentiable operations like exp, log, and matrix conversions for computer vision and robotics applications.","skillfed_tags":["lie-groups","robotics","differentiable-geometry"],"use_cases":["Optimize camera poses or object transforms in 3D vision pipelines using manifold-aware gradient descent.","Compose and apply rigid body transformations in robotics simulation or control code with automatic differentiation.","Batch-process rotations and transforms across multiple frames or trajectories using vmap.","Convert between rotation representations (quaternions, matrices, Euler angles) while maintaining differentiability.","Implement factor graph optimization for SLAM or pose estimation with Lie group constraints."],"what_it_does":"jaxlie is a JAX library for working with matrix Lie groups commonly used in rigid body kinematics and transformations. It provides high-level dataclass implementations of SO2 (2D rotations), SE2 (2D rigid transforms), SO3 (3D rotations), and SE3 (3D rigid transforms), each parameterized in a way suitable for optimization and differentiation. Every group supports forward and reverse-mode autodiff-friendly operations including exp, log, matrix conversion, composition, and inversion.\n\nThe library is designed for computer vision and robotics workflows where you need to optimize over transformation manifolds or compose transformations in differentiable code. It integrates with JAX's function transformations (vmap, jit, grad) and supports broadcasting, pytree flattening, and serialization via flax. Taylor approximations handle numerical stability near singularities, and utilities like uniform random sampling and Euler angle conversion are included for SO3.","worth_installing":"Yes, if you are building JAX-based computer vision or robotics code that requires differentiable rigid body transformations. The library is well-designed for manifold optimization and integrates cleanly with JAX's ecosystem. The aging maintenance status (477 days since last release) is not a blocker\u2014the package is stable and the repo remains active\u2014but check whether recent JAX API changes affect your use case. No known vulnerabilities."},"id":"jaxlie","links":{"html":"https://skillfed.io/packages/jaxlie","md":"https://skillfed.io/packages/jaxlie.md","pypi":"https://pypi.org/project/jaxlie/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-04-24","license_spdx":null,"license_treatment":"permissive","name":"jaxlie","python_support":"supports_current","summary":"Matrix Lie groups in JAX"},"popularity":{"monthly_downloads":96173,"position":13225,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.5.0"}