[SciPy-user] ANN: PyAMG v1.0 (Algebraic Multigrid Solvers in Python)
Nathan Bell
wnbell at gmail.com
Thu Mar 5 14:26:49 EST 2009
We are pleased to announce the first release of PyAMG: Algebraic
Multigrid Solvers in Python. With a user-friendly interface and
efficient implementation, PyAMG addresses the growing demand for
scalable solvers by non-experts. PyAMG features implementations of
* Ruge-Stuben (RS) or Classical AMG
* AMG based on Smoothed Aggregation (SA)
and experimental support for
* Adaptive Smoothed Aggregation (=E1SA)
* Compatible Relaxation (CR)
along with many tunable options for coarsening, interpolation,
relaxation, prolongator smoothing.
Our goal with the PyAMG project is to provide a framework for existing
AMG methods and to allow for quick testing and prototyping of
additional functionality and algorithms. Specifically, our objectives
are:
* ease of use
o interface is accessible to non-experts
o extensive documentation and references
* speed
o solves problems with millions of unknowns efficiently
o core multigrid algorithms are implemented in C++
o sparse matrix support is provided by scipy.sparse
* readability
o source code is organized into intuitive components
* extensibility
o core components can be reused to implement additional techniques
o new features are easy integrated
* experimentation
o facilitates rapid prototyping and analysis of multigrid methods
* portability
o tested on several platforms (Linux, Windows, Mac)
o relies only on Python, NumPy, and SciPy
PyAMG relies extensively on the NumPy and SciPy libraries for
scientific programming with Python. We thank the NumPy/SciPy
community for their support and continued efforts.
For more information see
http://www.pyamg.org
PyAMG developers:
Nathan Bell (http://graphics.cs.uiuc.edu/~wnbell/)
Luke Olson (http://www.cs.uiuc.edu/homes/lukeo/)
Jacob Schroder (www.cse.uiuc.edu/~jschrod3/)
--
Nathan Bell wnbell at gmail.com
http://graphics.cs.uiuc.edu/~wnbell/
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