An efficient method for computing eigenvalues of a real normal matrix
207. B. B. Zhou and R. P. Brent,
An efficient method for computing eigenvalues of a real normal matrix,
Journal of Parallel and Distributed Computing 63 (2003), 638-648.
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.
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