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

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