SVD Factorization (Not in Base Package)

Performs the singular value decomposition (SVD) of a given m-by-n real matrix A, with m>n. Details

A is an m-by-n matrix with m is greater than n, where m represents the number of rows in A, and n represents the number of columns in A. If A has m<n, transpose A before you call this VI. Or, you can create rows of zeroes underneath the nonzero rows in A, until A becomes square, and then call this VI.
U is an m-by-n matrix, which contains n orthogonal columns.
S is an array, which contains the number of n singular values of A in decreasing order.
V is an n-by-n orthogonal matrix.
error returns any error or warning condition from the VI.

SVD Factorization Details

SVD produces three matrices U,, and V so that

where U and are orthogonal matrices, is an n-by-n diagonal matrix with the elements of array S on the diagonal in decreasing order.