Factored Orthogonal Matrix-vector Multiplication with Applications to Parallel and Adaptive Eigennltering and Svd 1 Factored Orthogonal Matrix -vector Multiplication with Applications to Parallel and Adaptive Eigennltering and Svd

A novel algorithm is presented for adaptive eigennltering and for updating the singular value decomposition (SVD). It is an improvement upon an earlier developed Jacobi-type SVD updating algorithm, where now the exact orthogonality of the matrix of singular vectors/eigenvectors is guaranteed by storing a minimal factorization. This orthogonality property is known to be crucial for the numerical stability of the overall algorithm. The factored approach leads to a triangular array of rotation cells, implementing an orthogonal matrix-vector multiplication, and a novel array for SVD updating. Both Filiep Vanpoucke is a research assistant of the Belgian N. 1 arrays can be built up of CORDIC processors since the algorithms make exclusive use of orthogonal planar transformations.

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