PyAMG 5.3.1.dev20+g0c02134#

Installation#

PyAMG requires numpy and scipy

pip install pyamg

or from source:

pip install .

or with conda (see details below)

conda config --add channels conda-forge
conda install pyamg

For an editable install use:

pip install meson-python meson ninja pybind11 versioningit pytest
pip install --no-build-isolation -e .

Introduction#

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

PyAMG is currently developed and maintained by Luke Olson, Jacob Schroder, and Ben Southworth. The organization of the project can be found in ``organization.md` <organization.md>`_ and examples of use can be found in ``pyamg-examples` <pyamg/pyamg-examples>`_.

Acknowledgements: PyAMG was created by Nathan Bell, Luke Olson, and Jacob Schroder. Portions of the project were partially supported by the NSF under award DMS-0612448.

Citing#

If you use PyAMG in your work, please consider using the following citation:

@article{pyamg2023,
  author    = {Nathan Bell and Luke N. Olson and Jacob Schroder and Ben Southworth},
  title     = {{PyAMG}: Algebraic Multigrid Solvers in Python},
  journal   = {Journal of Open Source Software},
  year      = {2023},
  publisher = {The Open Journal},
  volume    = {8},
  number    = {87},
  pages     = {5495},
  doi       = {10.21105/joss.05495},
  url       = {https://doi.org/10.21105/joss.05495},
}

Getting Help#

For documentation see http://pyamg.readthedocs.io/en/latest/.
Create an issue.
Look at the Tutorial or the examples (for instance the 0_start_here example).
Run the unit tests (pip install pytest):
- From Python with PyAMG installed and from a non-source directory: .. code-block:: python
  
  import pyamg pyamg.test()
- From the PyAMG source directory and an editable install: (e.g. with pip install -e .):
```
pip install --no-build-isolation -e ".[test]"
pytest .
```
- From the PyAMG source directory and a regular install: (e.g. with pip install -e .):
```
pip install ".[test]"
pytest --pyargs pyamg --basetemp=temp
```

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-10
print("residual: ", np.linalg.norm(b-A@x))          # compute norm of residual vector

Program output:

MultilevelSolver
Number of Levels:     9
Operator Complexity:   2.198
Grid Complexity:       1.667
Coarse Solver:        'pinv'
  level   unknowns     nonzeros
     0      250000      1248000 [45.50%]
     1      125000      1121002 [40.87%]
     2       31372       280840 [10.24%]
     3        7814        69818 [2.55%]
     4        1953        17559 [0.64%]
     5         481         4421 [0.16%]
     6         123         1145 [0.04%]
     7          30          282 [0.01%]
     8          10           76 [0.00%]

residual:  4.772211803542084e-09

Conda#

More information can be found at conda-forge/pyamg-feedstock.

Linux:

OSX:

Windows:

Version:

Downloads:

Installing pyamg from the conda-forge channel can be achieved by adding conda-forge to your channels with:

conda config --add channels conda-forge

Once the conda-forge channel has been enabled, pyamg can be installed with:

conda install pyamg

It is possible to list all of the versions of pyamg available on your platform with:

conda search pyamg --channel conda-forge

Reference#

PyAMG Reference