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An efficient method for computing eigenvalues of a real normal matrix
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DOI：10.1016/S0743-7315(03)00007-8.png)
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Abstract
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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 Copyright (C) 2003 Published by Elsevier Science (USA). All rights reserved.
Keywords:
eigenvalue decomposition
normal matrix
Jacobi algorithm and QR algorithm
parallel computing
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