sci AMG is a multilevel technique for solving large-scale linear systems with optimal or near-optimal efficiency. Unlike geometric multigrid, AMG requires little or no geometric information about the underlying problem and develops a sequence of coarser grids directly from the input matrix. This feature is especially important for problems discretized on unstructured meshes and irregular grids. PyAMG features implementations of: * Ruge-Stuben (RS) or Classical AMG * AMG based on Smoothed Aggregation (SA) and experimental support for: * Adaptive Smoothed Aggregation (αSA) * Compatible Relaxation (CR) The predominant portion of PyAMG is written in Python with a smaller amount of supporting C++ code for performance critical operations.