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svd

Version:
    Last Modified: March 15, 2017

    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.

    Syntax

    sv = svd(a)
    sv = svd(a, 'econ')
    [u, s, v] = svd(a)
    [u, s, v] = svd(a, 'econ')

    Inputs

    a

    m-by-n matrix.

    econ

    Performs the decomposition in economical sized format.

    Outputs

    sv

    Singular values of a. sv is a real vector with min(m, n) elements.

    u

    m-by-min(m, n) unitary matrix.

    s

    Square matrix of order min(m, n) with the singular values on the main diagonal and zeros elsewhere.

    v

    n-by-min(m, n) unitary matrix.

    Singular Value Decomposition Details

    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').

    A = [1, 2, 3, 4; 5, 6, 7, 8; 9, 0, 1, 2; 3, 4, 5, 6];
    C = svd(A)

    Where This Node Can Run:

    Desktop OS: Windows

    FPGA: This product does not support FPGA devices


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