[SciPy-user] SciPy-user Digest, Vol 44, Issue 54

[fred]

Anand Patil a écrit :
>> Let me reiterate my support for the SVD. It allows you to find a
>> least-squares solution for matrices with nullspaces, real or
>> numerical, even if the result vector is not in the span of the matrix.
>> It also lets you check how far your answer is from being a real
>> solution, in both spaces.
>>
>>
>>
>> Anne
>>  
>>
>>     
> Anne,
>
> I completely agree with you, the SVD is very much nicer and more 
> intuitive to work with than the Cholesky decomposition. The only thing 
> Cholesky has to recommend it is that it's a lot faster for large 
> matrices, and in my applications (and possibly the OP's) the need for 
>   
Not possibly ;-)
I _do_ need for speed.

> Cheers,
> Anand
>
> In [9]: A=eye(1000)
>
> In [10]: %time b=svd(A)
> CPU times: user 11.54 s, sys: 0.69 s, total: 12.22 s
> Wall time: 19.25
>
> In [11]: %time b=cholesky(A)
> CPU times: user 0.53 s, sys: 0.16 s, total: 0.69 s
> Wall time: 0.89
I did not ask Anne to speed up filling my VTK arrays with broadcasting 
to loss all of it in
solving my matrices ;-)))

Cheers,

-- 
http://scipy.org/FredericPetit