# pyamg

**Repository Path**: fanronghong/pyamg

## Basic Information

- **Project Name**: pyamg
- **Description**: Algebraic Multigrid Solvers in Python
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-01-23
- **Last Updated**: 2025-10-21

## Categories & Tags

**Categories**: cae

**Tags**: None

## README

[![Build Status](https://img.shields.io/travis/pyamg/pyamg/master.svg?style=flat-square)](https://travis-ci.org/pyamg/pyamg)
[![Coverage Status](https://img.shields.io/codecov/c/github/pyamg/pyamg/master.svg?style=flat-square)](https://codecov.io/gh/pyamg/pyamg)
[![PyPi](https://img.shields.io/pypi/pyversions/pyamg.svg?style=flat-square)](https://pypi.python.org/pypi/pyamg/)

# Installation
PyAMG requires `numpy` and `scipy`

```
pip install pyamg
```

or

```
python setup.py install
```

or with conda (see details below)

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

# Introduction

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

![](./Docs/logo/PyAMG_logo.png)

PyAMG is currently developed by [Luke Olson](http://lukeo.cs.illinois.edu), and [Jacob Schroder](http://people.llnl.gov/schroder2).

# Citing

<pre>
@MISC{OlSc2018,
      author = "Olson, L. N. and Schroder, J. B.",
      title = "{PyAMG}: Algebraic Multigrid Solvers in {Python} v4.0",
      year = "2018",
      url = "https://github.com/pyamg/pyamg",
      note = "Release 4.0"
      }
</pre>

# Getting Help

For documentation see [http://pyamg.readthedocs.io/en/latest/](http://pyamg.readthedocs.io/en/latest/).

Create an [issue](https://github.com/pyamg/pyamg/issues).

Look at the [Tutorial](https://github.com/pyamg/pyamg/wiki/Tutorial) or the [Examples](https://github.com/pyamg/pyamg/wiki/Examples) (for instance  the [0STARTHERE](https://github.com/pyamg/pyamg-examples/blob/master/0STARTHERE/demo.py) 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.

````python
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:

<pre>
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
</pre>

# Conda

More information can be found at [conda-forge/pyamg-feedstock](https://github.com/conda-forge/pyamg-feedstock).

*Linux:*
[![Circle CI](https://circleci.com/gh/conda-forge/pyamg-feedstock.svg?style=shield)](https://circleci.com/gh/conda-forge/pyamg-feedstock)

*OSX:*
[![TravisCI](https://travis-ci.org/conda-forge/pyamg-feedstock.svg?branch=master)](https://travis-ci.org/conda-forge/pyamg-feedstock)

*Windows:*
[![AppVeyor](https://ci.appveyor.com/api/projects/status/github/conda-forge/pyamg-feedstock?svg=True)](https://ci.appveyor.com/project/conda-forge/pyamg-feedstock/branch/master)

*Version:*
[![Anaconda-Server Badge](https://anaconda.org/conda-forge/pyamg/badges/version.svg)](https://anaconda.org/conda-forge/pyamg)

*Downloads:*
[![Anaconda-Server Badge](https://anaconda.org/conda-forge/pyamg/badges/downloads.svg)](https://anaconda.org/conda-forge/pyamg)

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
```