Performs the singular value decomposition (SVD) of a given m-by-n, complex matrix A with m>n. Details
![]() |
A is a complex matrix of m-by-n with m is greater than or equal to 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 zeros 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 produces three matrices U, S, and V, so that
where U and V are orthogonal matrices, is an n-by-n diagonal matrix with the elements of array S on the diagonal in decreasing order. The diagonal elements are the singular values of A.