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The routine computes the singular value decomposition (SVD) of a rectangular real matrix A, optionally the left and/or right singular vectors. This routine uses a divide and conquer algorithm to compute SVD.
The SVD is written as:
A = U*SIGMA*VT
A is a real m-by-n matrix.
SIGMA is an m-by-n matrix which is zero except for its min(m,n) diagonal elements.
U is an m-by-m orthogonal matrix.
VT (V transposed) is an n-by-n orthogonal matrix.
The diagonal elements of SIGMA are the singular values of A; they are real and non-negative, and are returned in descending order. The first min(m,n) columns of U and V are the left and right singular vectors of A.
The routine returns VT, not V.
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