A Partitioning Strategy for Nonuniform Problems on Multiprocessors
IEEE Transactions on Computers
Using SWIG to Bind C++ to Python
Computing in Science and Engineering
Jacobian-free Newton-Krylov methods: a survey of approaches and applications
Journal of Computational Physics
An overview of the Trilinos project
ACM Transactions on Mathematical Software (TOMS) - Special issue on the Advanced CompuTational Software (ACTS) Collection
Matplotlib: A 2D Graphics Environment
Computing in Science and Engineering
PyTrilinos: High-performance distributed-memory solvers for Python
ACM Transactions on Mathematical Software (TOMS)
Anasazi software for the numerical solution of large-scale eigenvalue problems
ACM Transactions on Mathematical Software (TOMS)
FiPy: Partial Differential Equations with Python
Computing in Science and Engineering
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PyTrilinos is a set of Python interfaces to compiled Trilinos packages. This collection supports serial and parallel dense linear algebra, serial and parallel sparse linear algebra, direct and iterative linear solution techniques, algebraic and multilevel preconditioners, nonlinear solvers and continuation algorithms, eigensolvers and partitioning algorithms. Also included are a variety of related utility functions and classes, including distributed I/O, coloring algorithms and matrix generation. PyTrilinos vector objects are compatible with the popular NumPy Python package. As a Python front end to compiled libraries, PyTrilinos takes advantage of the flexibility and ease of use of Python, and the efficiency of the underlying C++, C and Fortran numerical kernels. This paper covers recent, previously unpublished advances in the PyTrilinos package.