On Oct 24, 2010, at 6:42 PM, Hao Xiong wrote:
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.
What operating system are you using? The sparse eigensolvers have some issues on 64-bit OS-X (see http://projects.scipy.org/scipy/ticket/1220). I am currently having similar issues. Oddly enough, I get reasonable results if I convert the matrix from the native float64 to the float32 data type. If you are on a 64 bit system, could you try that and let me know if it changes anything? For example, define A as A = W.dot(W).astype(numpy.float32) Best, Lutz