Factor matrix using singular value decomposition
Math Functions / Matrices and Linear Algebra / Matrix Factorizations
The Singular Value Decomposition block factors the M-by-N input matrix A such that
U is an M-by-P matrix
V is an N-by-P matrix
S is a length-P vector
P is defined as min(M,N)
M = N, U and V are both M-by-M unitary matrices
M > N, V is an N-by-N unitary matrix, and U is an M-by-N matrix whose columns are the first N columns of a unitary matrix
N > M, U is an M-by-M unitary matrix, and V is an N-by-M matrix whose columns are the first M columns of a unitary matrix
In all cases, S is an unoriented vector of positive singular values having length P.
Length-N row inputs are treated as length-N columns.
Note that the first (maximum) element of output S is equal to the 2-norm of the matrix A.
Select to enable the
Select to enable the E output port, which reports a failure to converge. The possible values you can receive on the port are:
0 — The singular value decomposition
1 — The singular value decomposition
calculation does not converge.
If the singular value decomposition calculation fails to converge, the output at ports U, S, and V are undefined matrices of the correct size.
Golub, G. H., and C. F. Van Loan. Matrix Computations. 3rd ed. Baltimore, MD: Johns Hopkins University Press, 1996.
|Port||Supported Data Types|
|Autocorrelation LPC||DSP System Toolbox|
|Cholesky Factorization||DSP System Toolbox|
|LDL Factorization||DSP System Toolbox|
|LU Inverse||DSP System Toolbox|
|Pseudoinverse||DSP System Toolbox|
|QR Factorization||DSP System Toolbox|
|SVD Solver||DSP System Toolbox|
See Matrix Factorizations for related information.