Hello,
I was trying to find the LU factorization for a sparse matrix(size 159990 by 159990). Following are the three lines in python i have used for the LU decomposition
"
from scipy.sparse import csc_matrix, linalg as sla
interior_stiff=CSC_matrix(159990 by 159990)
LU= sla.splu(interior_stiff,options=dict(SymmetricMode=True)) "
Properties of this matrix include,
1. Symmetric
2. The diagonal elements are the largest among the corresponding matrix column.
3. Non singular matrix
While running the code i was getting the following error in spyder GUI,
File "", line 1, in
runfile('C:/temp/new_LU/Run_1_using_LU.py', wdir='C:/temp/new_LU')
File "C:\ProgramData\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 866, in runfile
execfile(filename, namespace)
File "C:\ProgramData\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/temp/new_LU/Run_1_using_LU.py", line 574, in
LU= sla.splu(interior_stiff,options=dict(SymmetricMode=True))
File "C:\ProgramData\Anaconda3\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 261, in splu
ilu=False, options=_options)
RuntimeError: Factor is exactly singular
Can anyone suggest a possible solution for this?
Any help will be appreciated.
Thank You,
Paul Thomas
Hi, Paul!
Your matrix seems to be very ill-conditioned; Try to find singular values with sparse.svds...; Also, you can try to decrease matrix's condition number by dividing each column of the matrix on its Euclidean norm; After such scaling transformation each column of the matrix will have unity norm, and definitely lower cond. number (if it is really non-singular);
At last, the matrix is very large; Is it possible to do some preliminary investigations with a matrix of lower size?!
- Badges
- Science topic
- Similar topics
- Biological Science
- Kinesiology
- Running