An efficient method for computing eigenvalues of a real normal matrix
Date
2003
Authors
Zhou, B. B.
Brent, Richard
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Publisher
Academic Press
Abstract
Jacobi-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
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Keywords
Keywords: Eigenvalue decomposition; Jacobi algorithm and QR algorithm; Normal matrix; Parallel computing
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Source
Journal of Parallel and Distributed Computing
Type
Journal article
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Restricted until
2037-12-31
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