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 =
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 =
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
On Mon, 08 Nov 2010 13:23:46 -0600, Pauli Virtanen p...@iki.fi wrote: 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
On Mon, 08 Nov 2010 13:23:46 -0600, Pauli Virtanen p...@iki.fi wrote:
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
On 11/08/2010 01:38 PM, Joon wrote:
On Mon, 08 Nov 2010 13:23:46 -0600, Pauli Virtanen p...@iki.fi wrote:
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
On Mon, 08 Nov 2010 14:06:03 -0600, Bruce Southey wrote:
[clip]
Numpy uses SVD to get the (pseudo) inverse, which is usually very
accurate at getting (pseudo) inverse.
numpy.linalg.inv does
solve(a, identity(a.shape[0], dtype=a.dtype))
It doesn't use xGETRI since that's not included
On Mon, Nov 8, 2010 at 12:00 PM, Joon groups.and.li...@gmail.com wrote:
Another question is, is it better to do cho_solve(cho_factor(A), b) than
solve(A, b)?
If A is symmetric positive definite, then using the cholesky
decomposition should be somewhat faster than using a more general
solver.
On 8 November 2010 14:38, Joon groups.and.li...@gmail.com wrote:
Oh I see. So I guess in invA = solve(Ax, I) and then x = dot(invA, b) case,
there are more places where numerical errors occur, than just x = solve(Ax,
b) case.
That's the heart of the matter, but one can be more specific. You
Thanks, Nathaniel. Your reply was very helpful.
-Joon
On Mon, 08 Nov 2010 15:47:22 -0600, Nathaniel Smith n...@pobox.com wrote:
On Mon, Nov 8, 2010 at 12:00 PM, Joon groups.and.li...@gmail.com wrote:
Another question is, is it better to do cho_solve(cho_factor(A), b) than
solve(A, b)?
If A
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