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Computing the singular value decomposition of a product of two matrices. (English) Zbl 0607.65013
An algorithm for computing the singular value decomposition (SVD) of a product of two general matrices is given. SVD of an \(m\times n\) matrix A has the form \(A=U\Sigma V^ T\), where U and V are orthogonal matrices of order m and n respectively, and \(\Sigma\) is an \(m\times n\) nonnegative diagonal matrix. The algorithm is based on a Jacobi-like method due to Kogbetliantz and it is described in detail. The authors develop the basic method by using plane rotations applied to the two matrices separately. A triangular variant to compute the SVD is also given. The paper concludes with a discussion of implementation details and some test results.
Reviewer: D.Herceg

MSC:
65F15 Numerical computation of eigenvalues and eigenvectors of matrices
Software:
EISPACK
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