what linear algebra do you use most often for practical problems?
kurtosis: I apologize for threadjacking but you guys might be able to help.I have a different problem - I would like to compute an approximate SVD of a very large sparse matrix, (for spectral clustering) but I can't find a good implementation which works for datasets too large to fit in core.  This is a hadoop scale problem.  What's the best way to do this?Of course, finding all the singular values/vectors is out of the question, but I just need the top hundred or so.Does anyone here have any suggestions for how to do this?  The obvious strategy is just to construct a rank-100 approximation and optimize the singular values and vectors so that they get as close as possible to the real matrix. I guess gradient descent or something like that would work.  Are there existing packages that do this with hadoop?