Speaker
Ryan Schneider
(University of California Berkeley)
Description
While pseudospectral divide-and-conquer is optimal for nonsymmetric eigenvalue problems (in terms of both arithmetic and communication complexity) it is not currently implemented in any of our standard numerical linear algebra libraries. This is due to both the difficulty of translating the algorithm's technical pseudocode into something practical and to the challenge of preparing users for a randomized eigensolver, which will necessarily output different eigenvalues each time it runs. This talk explores the obstacles to bringing pseudospectral divide-and-conquer to practice and the reasons for pursuing a high-performance implementation in spite of them.
Author
Ryan Schneider
(University of California Berkeley)
Co-authors
Ioana Dumitriu
(University of California, San Diego)
James Demmel
(University of California Berkeley)