{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/18"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"},{"label":"Education","url":"https://skillfed.io/packages/category/education"}],"enrichment":{"capability":"PyAMG provides Algebraic Multigrid (AMG) solvers for efficiently solving large-scale sparse linear systems, with implementations of Classical AMG and Smoothed Aggregation methods.","skillfed_tags":["numerical-linear-algebra","sparse-matrices","scientific-computing"],"use_cases":["Solve 2D/3D Poisson problems and other elliptic PDEs on unstructured grids where geometric information is unavailable","Precondition iterative solvers for large sparse systems arising from finite-element discretizations","Accelerate convergence of Krylov subspace methods (e.g., GMRES, CG) via multigrid preconditioning","Prototype and develop multilevel solution strategies for research in numerical linear algebra"],"what_it_does":"PyAMG is a library of Algebraic Multigrid solvers designed to solve large-scale sparse linear systems with optimal or near-optimal efficiency. Unlike geometric multigrid, AMG requires little geometric information about the underlying problem and develops a sequence of coarser grids directly from the input matrix\u2014making it especially useful for problems on unstructured meshes and irregular grids. The library is written primarily in Python with performance-critical C++ components.\n\nThe package implements Classical (Ruge-Stuben) AMG and Smoothed Aggregation methods, with experimental support for Adaptive Smoothed Aggregation and Compatible Relaxation. It is commonly used as a preconditioner or standalone solver in scientific computing workflows, particularly for finite-element and finite-difference discretizations. Installation is straightforward via pip or conda, with prebuilt wheels for modern Python versions on major platforms.","worth_installing":"Yes. PyAMG is a mature, actively maintained library (active status, recent commits) with no known vulnerabilities, permissive MIT licensing, and solid platform coverage. Install friction is moderate due to compiled components, but prebuilt wheels mitigate this. Suitable for anyone solving large sparse linear systems, especially on unstructured problems where classical multigrid is impractical."},"id":"pyamg","links":{"html":"https://skillfed.io/packages/pyamg","md":"https://skillfed.io/packages/pyamg.md","pypi":"https://pypi.org/project/pyamg/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-08-24","license_spdx":null,"license_treatment":"permissive","name":"pyamg","python_support":"supports_current","summary":"PyAMG: Algebraic Multigrid Solvers in Python"},"popularity":{"monthly_downloads":141888,"position":11231,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"5.3.0"}