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

[Matthieu Brucher]

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
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