
SVD and eigenvalues
This is the question i asked before. I need some help here as i am very poor in computing. Some hints or help will be really appreciated.
Question: Let A be the $\displaystyle m \times m $ uppertriangular matrix with 0.1 on the main diagonal and 1 everywhere above the the diagonal. Write a programe to compute the smallest singular value of A in two ways. By calling a standard SVD software, and by forming A*A and computing the square root of its smallest eigenvalue. Run the program for $\displaystyle 1 \leq m \leq 30 $ and plot the result as two curves on a log scale. Do the result confirm to our general discussion of these algorithms?
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Re: SVD and eigenvalues
I need some help. Please help me.