Domain decomposition

Thursday, June 9, 2016 - 3:15pm - 4:15pm
Andrew Barker (Lawrence Livermore National Laboratory)
Optimization of controls and parameters coming from realistic full-scale simulation requires enormous computational effort. To make such optimization practical requires optimal multilevel solvers and scalable parallel algorithms. Even in the case where such solvers and algorithms are well understood for the forward problem, adapting them to the optimization context can be interesting and complicated.
Wednesday, December 1, 2010 - 2:45pm - 3:30pm
Olof Widlund (New York University)
In recent years, variants of the two-level Schwarz algorithm

have been developed in collaboration between Clark Dohrmann
Sunday, November 28, 2010 - 2:00pm - 3:30pm
David Keyes (King Abdullah University of Science & Technology)
Domain decomposition, a form of divide-and-conquer for mathematical problems
posed over a physical domain is the most common paradigm for large-scale
simulation on massively parallel, distributed, hierarchical memory
computers. In domain decomposition, a large problem is reduced to a
collection of smaller problems, each of which is easier to solve
computationally than the undecomposed problem, and most or all of which can
be solved independently and concurrently. Domain decomposition has proved to
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