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    Matrix : Convergence, Pseudoinverse and Single Value Decomposition

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    Only problems #3 &4-a,(without using any software).

    3 .
    Ax = b
    we consider the iterative scheme
    ....
    where the matrix Q is nonsingular.
    (a) If ... for some subordinate matrix norm, show that the sequence produced by the above scheme converges to the solution of the system for any initial vector x(0).

    4. Given singular matrix
    ....
    (a) Find its singular-value decomposition.
    (b) Find its pseudoinverse.

    © BrainMass Inc. brainmass.com October 9, 2019, 4:19 pm ad1c9bdddf
    https://brainmass.com/math/linear-algebra/matrix-convergence-pseudoinverse-single-value-decomposition-28989

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    The proofs for the theorems were taken from Atkinson

    The numerical work was verified by MAPLE

    Definition:

    Let A be an arbitrary square matrix of order n, the spectrum of A is the set of all the eigenvalues, and it is denoted as (A). The spectral radius is the maximum size of these eigenvalues and it is denoted by:

    Theorem 1:
    Let A be an arbitrary square matrix of order n. then for every matrix norm we have:

    .

    Proof:

    Let  be the largest eigenvalue of A (in absolute terms) and let v be the eigenvector associated with this eigenvalue with .
    Then:

    Theorem 2:

    Let A be an arbitrary square matrix of order n. Then Am converges to the zero matrix as m→∞ if and only if ...

    Solution Summary

    Matrix problems involving Convergence, Pseudoinverse and Single Value Decomposition are solved. The solution is detailed and well presented.

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