Jacobi-like Algorithms for Eigenvalue Decomposition of a Real Normal Matrix Using Real Arithmetic
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
An efficient method for computing eigenvalues of a real normal matrix
Journal of Parallel and Distributed Computing
Memory hierarchy exploration for accelerating the parallel computation of SVDs
Neural, Parallel & Scientific Computations
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In this paper we give evidence to show that in one-sided Jacobi SVD computation the sorting of column norms in each sweep is very important. Two parallel Jacobi orderings are described. These orderings can generate n(n-1)/2 different index pairs and sort column norms at the same time. The one-sided Jacobi SVD algorithm using these parallel orderings converges in about the same number of sweeps as the sequential cyclic Jacobi algorithm. Some experimental results on a Fujitsu AP1000 are presented. The issue of equivalence of orderings is also discussed.