Hyperbolic householder algorithms for factoring structured matrices:

Abstract: "We derive efficient algorithms for computing triangular decompositions of Hermitian matrices with small displacement rank using hyperbolic Householder matrices. These algorithms can be both vectorized and parallelized. Implementations along with performance results on an Alliant FX/8...

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Bibliographische Detailangaben
Hauptverfasser: Cybenko, George (VerfasserIn), Berry, Michael (VerfasserIn)
Format: Buch
Sprache:English
Veröffentlicht: Urbana, Ill. 1989
Schriftenreihe:Center for Supercomputing Research and Development <Urbana, Ill.>: CSRD report 877
Schlagworte:
Zusammenfassung:Abstract: "We derive efficient algorithms for computing triangular decompositions of Hermitian matrices with small displacement rank using hyperbolic Householder matrices. These algorithms can be both vectorized and parallelized. Implementations along with performance results on an Alliant FX/80, Cray X-MP/48, and Cray 2 are discussed. The use of Householder type transformations is shown to improve performance for problems with nontrivial displacement ranks. The general algorithm reduces to, in special cases, the well known Schur algorithm for factoring Toeplitz matrices and Elden's algorithm for efficient solution of least squares problems arising in the regularization of some ill-posed integral equations
It gives a Householder formulation to the class of algorithms based on hyperbolic rotations studied by Kailath, Lev-Ari, Chun and their colleagues for Hermitian matrices with small displacement structure.
Beschreibung:29 S.

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