[SciPy-User] Sparse Matricies and NNLS

[Calvin Morrison]

Hi!

I have a very large matrix that I am using with the
scipy.optimize.nnls function, however the matrix is so large that it
takes 30Gb of memory to load with python!

I was thinking about using a sparse matrix since it is relatively
sparse, but the problem is that even if I was to use the sparse
matricies, the nnls function only accepts a ndarray, not a sparse
matrix. (when I try and throw a sparse matrix at it I get an error.)

So of course I'd need to convert it to a dense array before passing it
into nnls, but that would totally void the whole point of a sparse
array, because then python would still have to allocate the full dense
matrix.

Does anyone have an idea about how to efficiently pass a matrix and
store it without blowing up the memory usage?

Thank you,

Calvin