Algebraic Multigrid Solvers in Python
C++ Python C CSS Makefile GCC Machine Description Other

README.md

Build Status Coverage Status PyPi Downloads

Installation

PyAMG requires numpy and scipy

  pip install pyamg

or

  python setup.py install

Introduction

PyAMG is a library of Algebraic Multigrid (AMG) solvers with a convenient Python interface.

PyAMG is developed by Nathan Bell, Luke Olson, and Jacob Schroder, in the Deparment of Computer Science at the University of Illinois at Urbana-Champaign. Portions of the project were partially supported by the NSF under award DMS-0612448.

Citing

@MISC{BeOlSc2011,
      author = "Bell, W. N. and Olson, L. N. and Schroder, J. B.",
      title = "{PyAMG}: Algebraic Multigrid Solvers in {Python} v3.0",
      year = "2015",
      url = "http://www.pyamg.org",
      note = "Release 3.0"
      }

Getting Help

Creat an issue.

Look at the Tutorial or the Examples (for instance the 0STARTHERE example)

What is AMG?

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

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.

Example Usage

PyAMG is easy to use! The following code constructs a two-dimensional Poisson problem and solves the resulting linear system with Classical AMG.

import pyamg
import numpy as np
A = pyamg.gallery.poisson((500,500), format='csr')  # 2D Poisson problem on 500x500 grid
ml = pyamg.ruge_stuben_solver(A)                    # construct the multigrid hierarchy
print(ml)                                           # print hierarchy information
b = np.random.rand(A.shape[0])                      # pick a random right hand side
x = ml.solve(b, tol=1e-10)                          # solve Ax=b to a tolerance of 1e-8
print("residual: ", np.linalg.norm(b-A*x))          # compute norm of residual vector

Program output:

multilevel_solver
Number of Levels:     9
Operator Complexity:  2.199
Grid Complexity:      1.667
Coarse Solver:        'pinv2'
  level   unknowns     nonzeros
    0       250000      1248000 [45.47%]
    1       125000      1121002 [40.84%]
    2        31252       280662 [10.23%]
    3         7825        70657 [ 2.57%]
    4         1937        17971 [ 0.65%]
    5          483         4725 [ 0.17%]
    6          124         1352 [ 0.05%]
    7           29          293 [ 0.01%]
    8            7           41 [ 0.00%]

residual:  1.24748994988e-08