An efficient method for computing eigenvalues of a real normal matrix

dc.contributor.authorZhou, B. B.
dc.contributor.authorBrent, Richard
dc.date.accessioned2015-12-08T22:38:44Z
dc.date.issued2003
dc.date.updated2015-12-08T10:10:11Z
dc.description.abstractJacobi-based algorithms have attracted attention as they have a high degree of potential parallelism and may be more accurate than QR-based algorithms. In this paper we discuss how to design efficient Jacobi-like algorithms for eigenvalue decomposition of a real normal matrix. We introduce a block Jacobi-like method. This method uses only real arithmetic and orthogonal similarity transformations and achieves ultimate quadratic convergence. A theoretical analysis is conducted and some experimental results are presented. Crown
dc.identifier.issn0743-7315
dc.identifier.urihttp://hdl.handle.net/1885/35928
dc.publisherAcademic Press
dc.sourceJournal of Parallel and Distributed Computing
dc.subjectKeywords: Eigenvalue decomposition; Jacobi algorithm and QR algorithm; Normal matrix; Parallel computing
dc.titleAn efficient method for computing eigenvalues of a real normal matrix
dc.typeJournal article
local.bibliographicCitation.lastpage648
local.bibliographicCitation.startpage638
local.contributor.affiliationZhou, B.B., Deakin University
local.contributor.affiliationBrent, Richard, College of Physical and Mathematical Sciences, ANU
local.contributor.authoremailu4241028@anu.edu.au
local.contributor.authoruidBrent, Richard, u4241028
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor010101 - Algebra and Number Theory
local.identifier.ariespublicationu4105084xPUB130
local.identifier.citationvolume63
local.identifier.doi10.1016/S0743-7315(03)00007-8
local.identifier.scopusID2-s2.0-0037707112
local.identifier.uidSubmittedByu4105084
local.type.statusPublished Version

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