[Numpy-discussion] Strange behaviour of linalg.svd() and linalg.eigh()

Matthieu Brucher matthieu.brucher@gmail....
Fri May 16 12:37:48 CDT 2008


Hi,

I tried using Matlab with the same matrix and its eig() function. It can
diagonalize the matrix with a correct result, which is not the case for
linalg.eigh().
Strange.

Matthieu

2008/4/17 Matthieu Brucher <matthieu.brucher@gmail.com>:

> Hi,
>
> Ive implemented the classical MultiDimensional Scaling for the scikit learn
> using both functions. Their behavior surprised me for "big" arrays (10000 by
> 10000, symmetric as it is a similarity matrix).
> linalg.svd() raises a memory error because it tries to allocate a
> (7000000,) array (in fact bigger than that !). This is strange because the
> test was made on a 64bits Linux, so memory should not have been a problem.
> linalg.eigh() fails to diagonalize the matrix, it gives me NaN as a result,
> and this is not very useful.
> A direct optimization of the underlying cost function can give me an
> adequate solution.
>
> I cannot attach the matrix file (more than 700MB when pickled), but if
> anyone has a clue, I'll be glad.
>
> Matthieu
> --
> French PhD student
> Website : http://matthieu-brucher.developpez.com/
> Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92
> LinkedIn : http://www.linkedin.com/in/matthieubrucher




-- 
French PhD student
Website : http://matthieu-brucher.developpez.com/
Blogs : http://matt.eifelle.com and http://blog.developpez.com/?blog=92
LinkedIn : http://www.linkedin.com/in/matthieubrucher
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://projects.scipy.org/pipermail/numpy-discussion/attachments/20080516/6c627691/attachment.html 


More information about the Numpy-discussion mailing list