An efficient method for computing eigenvalues of a real normal matrix
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...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of Parallel and Distributed Computing|
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