Mon, 08 Nov 2010 13:17:11 -0600, Joon wrote: > 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)?
Not necessarily slower, but it contains more numerical error. http://www.johndcook.com/blog/2010/01/19/dont-invert-that-matrix/ > 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. That's the same thing as computing the inverse matrix. -- Pauli Virtanen _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
