Is there a way to compute average norm after a series of trials?
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Suppose I have a matrix
and
I am solving a least-square problem using QR using my own algorithm.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/682003/image.png)
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/682008/image.png)
A and b are both generated randomly.
Is there a way to run this least square algorithm several time and at each run the norm
is computed and finally the average of all these norms is taken?
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/682013/image.png)
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Ben McMahon
2021년 7월 12일
I am a bit confused by your question, as x is not defined. If you want to run your algorithim for a set number of randomly generated matrices and vectors then something similar to the code below should work:
numIterations = 10; % Number of random samples of your matrix / vector combo you want to compute
for Iteration = 1:numIterations
A = rand(100,100);
b = rand(100,1);
x = myLeastSquareAlgo(A,b); % Your algorithim function that calculates 'x'.
normArray(Iteration) = norm(b-A*x);
end
meanNorm = mean(normArray);
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