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svd (MathScript RT Module Function)

LabVIEW 2012 MathScript RT Module Help

Edition Date: June 2012

Part Number: 373123C-01

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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.




Name Description
a Specifies an m-by-n matrix.
0 Directs LabVIEW to perform the decomposition in a reduced-size format.


Name Description
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)


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