Parallel methods for iterative matrix decompositions

Author

Götze, Jürgen

Author_Institution

Dept. of Comput. Sci., Yale Univ., New Haven, CT, USA

fYear

1991

fDate

11-14 Jun 1991

Firstpage

232

Abstract

Parallel methods for iterative matrix decompositions are developed by using the possibility of approximate (but still orthogonal) rotations together with suitable kinds of rotation schemes. Favorable choices of some of these approximations are applied together with suitable rotation schemes (factorized rotation scheme, shift-and-add rotation scheme) in order to obtain algorithms highly suited for parallel implementations. The author mainly deals with the Jacobi method for the symmetric eigenvalue problem, but the ideas presented can also be applied to other iterative matrix decomposition algorithms using orthogonal rotations

Keywords

convergence of numerical methods; eigenvalues and eigenfunctions; iterative methods; mathematics computing; matrix algebra; parallel algorithms; Jacobi method; decomposition algorithms; factorized rotation scheme; iterative matrix decompositions; orthogonal rotations; parallel implementations; symmetric eigenvalue problem; Computer science; Concurrent computing; Convergence; Eigenvalues and eigenfunctions; Iterative algorithms; Iterative methods; Jacobian matrices; Matrix decomposition; Process design; Symmetric matrices;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1991., IEEE International Sympoisum on

Print_ISBN

0-7803-0050-5

Type

conf

DOI

10.1109/ISCAS.1991.176316

Filename

176316