[SciPy-user] Sparse v. dense matrix, SVD and LSI-like analysis
pearu at scipy.org
Wed Nov 17 14:31:11 CST 2004
On Wed, 17 Nov 2004, Nick Arnett wrote:
> Travis Oliphant wrote:
>> So, while the SVD is not an eigenvector decomposition it is related to one.
> Right... but I still don't understand the statement, "In any case, all of the
> linalg.* functions only operate on dense arrays, not sparse matrices."
The statement means that the current *implementation* of linalg
functions cannot operate on sparse matrices. Mathematically there is no
difference between sparse or dense matrices. Sparse or dense are notions
of matrix representation.
> Why would linalg.svd not operate on a sparse matrix? I was working from the
> example (below) from your Scipy tutorial, in fact. Would the results not be
> meaningful if the matrix is sparse?
Of course they are. Just with the current implementation one must
transform a sparse matrix to a full matrix (that may contain lots of
zeros) before applying linalg functions.
I believe that in future linalg functions will support sparse matrices as
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