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.
Type of simulation to run. You can set this parameter to:
Interpreted execution (default)
Simulate model using the MATLAB® interpreter. This option shortens startup time.
Simulate model using generated C code. The first time you run a simulation, Simulink® generates C code for the block. The C code is reused for subsequent simulations, as long as the model does not change. This option requires additional startup time.
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.