[SciPy-User] Eigenvectors of sparse symmetric matrix

[Hao Xiong]

Hi everyone,

I am trying to compute the eigenvectors corresponding to the d+1 smallest
eigenvalues of A=W.T*W. I started with W as a dense matrix and then
W = sparse.csr_matrix(W)
A = W.dot(W) # W.T * W
W,V = eigen_symmetric(A,d+1, which='SM')

The biggest problem is that the algorithm fails to converge and I get
all zeros as eigenvectors for a testing dataset. Using dense SVD I
got the expected results.

The second problem is that this sparse version is much slower than
the dense version as
  u,s,vh = svd(W)

The testing data only has 1000x1000, while I expect the real data
will have millions by millions of entries. Each row will have only
a dozen to at most dozes of non-zero entries.

Thanks,
Hao