# [Numpy-discussion] Solving Ax = b: inverse vs cholesky factorization

Joon groups.and.lists@gmail....
Mon Nov 8 16:43:12 CST 2010

```Thanks, Nathaniel. Your reply was very helpful.

-Joon

On Mon, 08 Nov 2010 15:47:22 -0600, Nathaniel Smith <njs@pobox.com> wrote:

> On Mon, Nov 8, 2010 at 12:00 PM, Joon <groups.and.lists@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. (Because, basically, the cholesky decomposition routine
> "knows" that your matrix is symmetric, so it only has to "look at"
> half of it, while a generic solver routine has to "look at" your whole
> matrix regardless). And indeed, that seems to be the case in numpy:
>
> In [18]: A = np.random.normal(size=(500, 500))
> In [19]: A = np.dot(A, A.T)
> In [20]: b = np.random.normal(size=(500, 1))
>
> In [21]: %timeit solve(A, b)
> 1 loops, best of 3: 147 ms per loop
>
> In [22]: %timeit cho_solve(cho_factor(A), b)
> 10 loops, best of 3: 92.6 ms per loop
>
> Also of note -- going via the inverse is much slower:
>
> In [23]: %timeit dot(inv(A), b)
> 1 loops, best of 3: 419 ms per loop
>
> (I didn't check what happens if you have to solve the same set of
> equations many times with A fixed and b varying, but I would still use
> cholesky for that case. Also, note that for solve(), cho_solve(),
> etc., b can be a matrix, which lets you solve for many different b
> vectors simultaneously.)
>
> -- Nathaniel
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