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Proceedings Paper

Computing the generalized singular value decomposition on the Connection Machine
Author(s): Haesun Park; L. Magnus Ewerbring
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Paper Abstract

A new algorithm for computing the generalized singular value decomposition that diagonalizes two matrices is introduced. First two existing algorithms are studied one due to Paige and the other to Han and Veselic. The former requires only orthogonal plane transformations resulting in two matrices with parallel rows. The latter employs nonsingular (not necessarily orthogonal) transformations to directly diagonalize the two data matrices. For many applications it is preferable to be given both the diagonal matrices and the txansformation matrices explicitly. Our algorithm has the advantages that the nonsingular transformation is readily available computation of transformations is simple convergence test is efficient in a parallel computing environment and the condition of each nonsingular transformation can be controlled. We present implementation results obtained on a Connection Machine 2 to compare our new algorithm with that of Paige.

Paper Details

Date Published: 1 November 1990
PDF: 12 pages
Proc. SPIE 1348, Advanced Signal Processing Algorithms, Architectures, and Implementations, (1 November 1990); doi: 10.1117/12.23495
Show Author Affiliations
Haesun Park, Univ. of Minnesota (United States)
L. Magnus Ewerbring, Argonne National Lab. (United States)

Published in SPIE Proceedings Vol. 1348:
Advanced Signal Processing Algorithms, Architectures, and Implementations
Franklin T. Luk, Editor(s)

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