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A parallel algorithm for computing the singular value decomposition of a matrix is presented. The algorithm uses a divide and conquer procedure based on a rank one modification of a bidiagonal matrix. Numerical difficulties associated with forming the product of a matrix with its transpose are avoided, and numerically stable formulae for obtaining the left singular vectors after computing updated right singular vectors are derived. A deflation technique is described that, together with a robust root finding method, assures computation of the singular values to full accuracy in the residual and also assures orthogonality of the singular vectors.
Jessup et al. (Fri,) studied this question.