Hi, I have a matrix M, quite a few (< 1/4th) of its eigen values are of O(10^-16). Analytically I know that M is positive definite, but numerically of course it is not. Some of the small (O(10^-16)) eigen values (as obtained from eigen()) are negative. It is the near-singularity that is causing the numerical errors. I could use svd(), but then the left ($u) and right ($v) vectors are not identical, again numerical errors. Is there any function that imposes pd property while calculating the eigen decomposition.
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