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Rare choles decomposition with replaced rows and columns

I am using scikits.sparse.cholmod for python to get cholesky factorization of a symmetric matrix.

I compared the results of cholesky () with matlab chol () . The results have a mismatch between some rows and columns. I am trying to iterate over factorization to get eigenvalues, and this mismatch seems problematic.

Here is my code:

import numpy as np
from scipy.sparse import csr_matrix
from scipy.sparse import csc_matrix
from scikits.sparse.cholmod import cholesky

A = csr_matrix([[1,2,0,0], [0,0,3, 0], [4,0,5, 0], [0, 0, 1, 2]])
B = (A*A.T)
print "B: "
print B.todense()

for i in range(10):
    factor = cholesky(B.tocsc())
    l = factor.L()  #l is lower triangular
    B = (l.T*l)
    print l.todense()

And the lower triangular matrix for the first iteration:

[[ 2.23606798  0.         0.          0.        ]
[ 0.          3.          0.          0.        ]
[ 0.          1.          2.          0.        ]
[ 1.78885438  5.          0.          3.57770876]]

And the lower triangular Matlab matrix:

[2.2361        0         0         0
     0    3.0000         0         0
1.7889    5.0000    3.5777         0
     0    1.0000         0    2.0000]

The result of Matlab is plausible, as it leads to the correct eigenvalues. Am I doing something wrong with choosing sparse matrix type in python?

+4
1

. P. , LL'=PBP'.

.

P, :

>>> factor.P()
array([0, 1, 3, 2], dtype=int32)

. .

+1

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