Singular Value Decomposition and Generalized Inverse

The SVD of an m by n matrix A is a matrix decomposition, A = USV^T. With q = min(m, n), the factors U_{m x q} and V_{n x q} are orthogonal matrices, and S_{q x q} is a nonnegative diagonal matrix with nonincreasing diagonal terms. The IMSL_SVDCOMP function computes the singular values of A by default. By using keywords, you can also obtain part or all of the U and V matrices, an estimate of the rank of A, and the generalized inverse of A.

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