A Systolic Array for SVD Updating
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A singular value decomposition (SVD) updating algorithm supplemented with a certain re-orthogonalization scheme is implemented on a systolic array with \(O(n^ 2)\) parallelism for \(O(n^ 2)\) complexity, by combining systolic implementations for the matrix-vector product, the QR updating and the SVD. It is shown that a main computational bottleneck for the array implementation can be overcome with a square root-free SVD algorithm based on modified Givens rotations and approximate schemes for the computation of rotation angles in the SVD steps.
- KU Leuven Belgium
- Katholieke Universiteit Leuven Belgium
Numerical computation of eigenvalues and eigenvectors of matrices, Complexity and performance of numerical algorithms, systolic array, singular value decomposition updating algorithm, recursive least squares, parallel algorithms, Parallel numerical computation, re-orthogonalization scheme, Givens rotations, complexity, Orthogonalization in numerical linear algebra
Numerical computation of eigenvalues and eigenvectors of matrices, Complexity and performance of numerical algorithms, systolic array, singular value decomposition updating algorithm, recursive least squares, parallel algorithms, Parallel numerical computation, re-orthogonalization scheme, Givens rotations, complexity, Orthogonalization in numerical linear algebra
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