Owning Class: linalgebra
Requires: MathScript RT Module
sv = svd(a)
sv = svd(a, 0)
[u, s, v] = svd(a)
[u, s, v] = svd(a, 0)
Performs singular value decomposition to compute the four fundamental subspaces of a matrix, namely the right and left null spaces and the right and left image spaces.
|a||Specifies an m-by-n matrix.|
|0||Directs LabVIEW to perform the decomposition in a reduced-size format.|
|sv||Returns the singular values of a. sv is a real vector with min(m, n) elements.|
|u||Returns an m-by-min(m, n) unitary matrix.|
|s||Returns a square matrix of order min(m, n) with the singular values on the main diagonal and zeros elsewhere.|
|v||Returns an n-by-min(m, n) unitary matrix.|
Singular value decomposition is a computationally expensive but powerful algorithm for solving a number of problems, including finding least square solutions, finding the 2-norm and 2-norm condition estimate, and determining the rank of a matrix. svd computes unitary matrices u and v such that the input matrix is equivalent to u*s*conjugate(v').
The following table lists the support characteristics of this function.
|Supported in the LabVIEW Run-Time Engine||Yes|
|Supported on RT targets||Yes|
|Suitable for bounded execution times on RT||Not characterized|
A = [1, 2, 3, 4; 5, 6, 7, 8; 9, 0, 1, 2; 3, 4, 5, 6]
C = svd(A)