Trilinos is Sandia's
allpurpose, objectoriented, parallel solver for the solution of
largescale, complex multiphysics engineering and scientific
applications.
Since my arrival at SNL, I have become a Trilinos developer
.
My particular focus is...
ML: Multilevel Preconditioning
ML
is the multilevel preconditioning package in Trilinos. It implements
smoothed aggregation algebraic multigrid (AMG) and is an
honesttogoodness industrial strength AMG code. It even runs in
parallel using MPI. I've recently put together a MATLAB interface for ML called MLMEX.
It's in the release version of Trilinos from 7.0 on.
I've primarily focused on multigrid solvers for Maxwell's
equations. This new solver (known as RefMaxwell) was released in Trilinos 8.0.
For more information on these techniques, consult An Algebraic Multigrid Approach Based on a Compatible Gauge
Reformulation of Maxwell's Equations [SAND20071633J], by P. Bochev, J. Hu, C. Siefert and R. Tuminaro (March 2007).
Generalized SaddlePoint Preconditioning Toolkit
The Generalized SaddlePoint Preconditioning Toolkit is a C/C++ code for
preconditioning generalized saddlepoint problem. It works with SuperLU
3.0 for direct solves and the Structured Probing Toolkit. It also has
builtin ILU/ILUT builtin (from SPARSKIT).
Structured Probing Toolkit
The Structured
Probing Toolkit provides the tools necessary for performing
probing on matrices with nonbanded structure. The essence of this
technique is to choose an a priori sparsity pattern based on knowledge
of the target matrix, and then use graph coloring techniques to choose
the probing vectors such that a matrix of that sparsity pattern would
be reconstructed exactly. If the “big” entries in the matrix
correspond to a certain graph, we can choose the a priori sparsity
pattern based on the edge locality of the graph.
A parallel structured probing method built on Zoltan's coloring routines
has been part of Isorropia since the release of Trilinos 10.0 in Fall 2009.
For more information on this technique, consult
Probing Methods for SaddlePoint Problems [Technical Report UIUCDCSR20052540], by C. Siefert and E. de Sturler (ETNA, Volume 22, pp.
163183, April 2006).
MAPS: ModelAssisted Pattern Search
In the context of derivativefree optimization, there are
circumstances under which the function evaluations are so expensive,
that building a really good model of the problem and then optimizing
that is a pretty good idea. For those situations, MAPS may
be the package for you. MAPS was developed by Chris Siefert and Amy
Yates under the direction of Virginia Torczon.
For more information on this technique, consult
ModelAssisted Pattern Search Methods for Optimizing Expensive
Computer Simulations by C. Siefert, V. Torczon and M.W. Trosset (Proceedings of the Section on Physical and Engineering Sciences, American Statistical Association, 2002).
