pyamg

library5.3.0pypypi✓ verified 87d ago

PyAMG is a Python library providing implementations of Algebraic Multigrid (AMG) solvers and supporting tools for approximating solutions to large, sparse linear systems of algebraic equations (Ax=b). It is actively maintained, with the current version being 5.3.0, and receives regular updates to support newer Python and SciPy versions and introduce new features.

data ai-ml

pip install pyamg

github ↗package ↗

INSTALL

IMPORT

SIG · PYAMG

pyamg

datapythonv5.3.0

Install

7.3s avg

Import

1314ms

Disk

238MB

Pass rate

8/ 10

Env Coverage8 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v5.3.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✓ —

✓ 7.25s

py 3.11

✓ —

✓ 7.15s

py 3.12

✓ —

✓ 7.15s

py 3.13

✓ —

✓ 7.5s

py 3.9

✕ build_error

238MB installed

● package 238MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

pyamg

✓ import pyamg

smoothed_aggregation_solver

✓ from pyamg import smoothed_aggregation_solver

gallery

✓ from pyamg import gallery

✗ import pyamg.gallery

While 'import pyamg.gallery' works, importing specific modules like 'gallery' directly is common for clarity and consistency with examples.

This quickstart demonstrates how to set up a 2D Poisson problem using PyAMG's gallery, construct a `smoothed_aggregation_solver`, and solve the resulting sparse linear system. It highlights the typical workflow for using PyAMG as a standalone solver.

import numpy as np from scipy.sparse import csr_matrix from pyamg import gallery from pyamg import smoothed_aggregation_solver # 1. Create a sample sparse matrix (e.g., 2D Poisson problem) A = gallery.poisson((100, 100), format='csr') # 2. Create a right-hand side vector b = np.random.rand(A.shape[0]) # 3. Construct the Algebraic Multigrid solver # max_coarse controls the size of the coarsest grid ml = smoothed_aggregation_solver(A, max_coarse=10) # 4. Solve the linear system Ax = b x = ml.solve(b, tol=1e-10) print(f"Matrix shape: {A.shape}") print(f"Number of nonzeros: {A.nnz}") print(f"Solution vector norm: {np.linalg.norm(x)}") print(f"Residual norm: {np.linalg.norm(b - A @ x)}")

Debug

Known issues

breakingIn PyAMG v5.3.0, the internal matrix storage naming convention changed from `[bc]sr_matrix` to `[bc]sr_array`. Additionally, the matrix-vector multiplication operator should now use `@` instead of `*` for NumPy compatibility.

fix

Update code to use `scipy.sparse.csr_array` (or `bsr_array`) instead of `csr_matrix` where relevant, and replace `A * x` with `A @ x` for matrix-vector products.

affects: >=5.3.0

breakingStarting from PyAMG v5.1.0, the minimum required Python version is 3.8 and the minimum required SciPy version is 1.8.0. Codebases using older versions of Python or SciPy may encounter compatibility issues.

fix

Ensure your Python environment is at least 3.8 and SciPy is version 1.8.0 or newer. For current PyAMG (v5.3.0), Python >=3.9 is required.

affects: >=5.1.0

gotchaFor optimal performance, especially with iterative solvers, it's best practice to create the `MultilevelSolver` object once and then call its `solve()` method multiple times for different right-hand sides, rather than re-creating the solver for each solve operation.

fix

Instantiate the `smoothed_aggregation_solver` (or other solver) once, then reuse the `ml.solve()` method. For example: `ml = smoothed_aggregation_solver(A); x1 = ml.solve(b1); x2 = ml.solve(b2)`.

affects: All

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'pyamg.amg_core.evolution_strength'

This error often indicates an incomplete or incorrect installation of PyAMG, particularly if installed in isolated or non-standard Python environments (e.g., custom Spyder installers or virtual environments without proper build tools). The underlying C++ extensions (`amg_core`) were not compiled or linked correctly.

fix

Reinstall PyAMG in a clean virtual environment using `pip install pyamg`. Ensure `numpy` and `scipy` are installed first. If issues persist, try installing from source (refer to PyAMG's GitHub for build instructions). For Anaconda users, `conda install -c anaconda pyamg` is an alternative.

Error in TokenizeString(): two or more options before an '=' sign in the configuration file.

This error is typically encountered when PyAMG is integrated with external software like SU2 for mesh adaptation, indicating a syntax error in the configuration file used by the calling application. It implies incorrect parsing of input parameters or missing required options (e.g., `PYADAP_COMPLEXITY`).

fix

Carefully review the configuration file (e.g., `.cfg` file for SU2) for any malformed lines, duplicate options, or missing parameters required by the PyAMG integration. Ensure all options are correctly formatted (e.g., `OPTION = VALUE`). Refer to the specific integration's documentation for required parameters.

MemoryError: Unable to allocate ...

PyAMG solvers can consume significant memory for large-scale problems, especially when generating coarse grids. This error occurs when the system runs out of available memory.

fix

For `smoothed_aggregation_solver`, try reducing the `max_levels` parameter to limit the number of coarse grids, or increasing `max_coarse` to allow coarser grids to be larger, reducing the total memory footprint. Using the CSR matrix format is also recommended for efficiency.

Upgrade

Version history

5.3.0latest on PyPI · released Aug 24, 2025

Audit

Dependencies

numpyrequiredRequired for numerical operations and array manipulation, specifically for handling matrices and vectors.

scipyrequiredRequired for sparse matrix support and operations, fundamental to PyAMG's functionality.

matplotliboptionalOften used for visualizing results and convergence histories in examples and tutorials.

pymetisoptionalOptional dependency added in v5.3.0 for enhanced aggregation capabilities.

Agent activity

9 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/pyamg/pyamg ↗

packagehttps://pypi.org/project/pyamg/ ↗