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Hi, I was wondering when it is better to store cholesky factor and use it to solve Ax = b, instead of storing the inverse of A. (A is a symmetric, positive-definite matrix.) Even in the repeated case, if I have the inverse of A (invA) stored, then I can solve Ax = b_i, i = 1, ... , n, by x = dot(invA, b_i). Is dot(invA, b_i) slower than cho_solve(cho_factor, b_i)? I heard calculating the inverse is not recommended, but my understanding is that numpy.linalg.inv actually solves Ax = I instead of literally calculating the inverse of A. It would be great if I can get some intuition about this. Thank you, Joon |
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