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(empirical) relation between MSE and condition number

  1. Feb 28, 2014 #1
    Hi,

    It is a well known fact that in an inverse linear problem low condition numbers have low noise amplification and therefore decrease the error.

    So I wanted to test this: I draw random (skinny) matrices A, calculate y=A*c where c is a known coefficient vector, add some noise and calculate c from Least Squares. I would expect at least a small correlation between the MSE for c and the condition number.

    But this is what it looks:

    untitled.png

    Yes, is it arbitrary, uncorrelated, this does not make sense at all! For example, a (relatively) low condition number of 1.5 can produce everything from the best (-79dB) down to the worst (-56dB). Changing the parameters does not change anything

    Can anyone tell me what I am doing wrong or which (wrong?) assumptions I make?

    Thanks


    PS: Here is the MATLAB code

    Code (Text):

    K = 5;
    M = 50;
    numtrials = 1000;
    c = randn(K,1);
    for trial=1:numtrials
        A = randn(M,K);
        y = A*c;
        y = add_noise(y, 55); % add 55dB noise via randn(...)
        c_rec = A \ y; %c_rec = pinv(A)*y;
        NMSE_c = 20*log10(norm(c - c_rec)/norm(c));
        plot(cond(A), NMSE_c, 'bo');
        hold on;
        xlabel('Condition number');
        ylabel('NMSE of coefficients');
        drawnow;
    end
     
     
  2. jcsd
  3. Mar 8, 2014 #2
    Well, this is not my field but I will try to give you some advices. What's happen if you add a 0db noise ? Also, are you sure that the function add_noise is bug free ?
     
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