On 5/12/07, Dave P. Novakovic <[EMAIL PROTECTED]> wrote:
Hi, I have test data of about 75000 x 75000 dimensions. I need to do svd, or at least an eigen decomp on this data. from search suggests to me that the linalg functions in scipy and numpy don't work on sparse matrices. I can't even get empty((10000,10000),dtype=float) to work (memory errors, or too many dims), I'm starting to feel like I'm in a bit of trouble here :)
Umm, big. What do people use to do large svd's? I'm not adverse to using another
lib or wrapping something.
What sort of machine do you have? There are column iterative methods for svd that resemble Gram-Schmidt orthogonalization that could probably be adapted to work over the array one column at a time. Are your arrays actually sparse? Do you only need a few eigenvalues? Are you doing least squares? A more precise description of the problem might lead to alternative , less demanding, approaches. Chuck
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