Table Of Contents

Generalized SVD Decomposition (G Dataflow)

Version:
    Last Modified: March 15, 2017

    Computes the generalized singular value decomposition (GSVD) of a matrix pair.

    connector_pane_image
    datatype_icon

    matrix A

    A matrix with m rows and p columns.

    This input accepts a 2D array of double-precision, floating point numbers or 2D array of complex double-precision, floating point numbers.

    datatype_icon

    matrix B

    A matrix with n rows and p columns.

    This input accepts a 2D array of double-precision, floating point numbers or 2D array of complex double-precision, floating point numbers.

    datatype_icon

    singular values only?

    A Boolean specifying whether the node computes only the generalized singular values.

    True Computes only the generalized singular values of the matrix pair.
    False Computes all matrices in the generalized SVD decomposition.

    Default: False

    datatype_icon

    SVD option

    Value specifying how the node performs the decomposition.

    Name Value Description
    Thin 0

    Decomposes matrix A as the multiplication of matrix U (m x min(m,p)), C (min(m,p) x p) and transpose of R (p x p).

    Decomposes matrix B as the multiplication of matrix V (n x min(n,p)), S (min(n,p) x p) and transpose of R (p x p).

    Full 1

    Decomposes matrix A as the multiplication of matrix U (m x m), C (m x p) and transpose of R (p x p).

    Decomposes matrix B as the multiplication of matrix V (n x n), S (n x p) and transpose of R (p x p).

    Default: Thin

    datatype_icon

    error in

    Error conditions that occur before this node runs.

    The node responds to this input according to standard error behavior.

    Standard Error Behavior

    Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

    error in does not contain an error error in contains an error
    If no error occurred before the node runs, the node begins execution normally.

    If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

    If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

    Default: No error

    datatype_icon

    singular values

    Generalized singular values of the input matrix pair (matrix A, matrix B).

    datatype_icon

    matrix U

    The U matrix of the GSVD results.

    datatype_icon

    matrix V

    The V matrix of the GSVD results.

    datatype_icon

    matrix C

    The C matrix of the GSVD results.

    datatype_icon

    matrix S

    The S matrix of the GSVD results.

    datatype_icon

    error out

    Error information.

    The node produces this output according to standard error behavior.

    Standard Error Behavior

    Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

    error in does not contain an error error in contains an error
    If no error occurred before the node runs, the node begins execution normally.

    If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

    If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.
    datatype_icon

    matrix R

    The R matrix of the GSVD results.

    Algorithm for Calculating Generalized SVD Decomposition

    The following expressions define the generalized singular value decomposition of a matrix pair (A, B).

    A = UCR'

    B = VSR'

    where U and V are orthogonal matrices and R is a square matrix.

    When k is the rank of matrix ( A B ) , then the first k diagonal elements of matrix CC + SS are ones and all of the other elements are zeros. The square roots of the first k diagonal elements of CC and SS determine the numerators and denominators of the generalized singular values, respectively.

    Where This Node Can Run:

    Desktop OS: Windows

    FPGA: This product does not support FPGA devices


    Recently Viewed Topics