we are using root version 4.00-02 on a Linux 2.4.20-31.9 (Red Hat 9).
When using the TDecompSVD class for singular value decomposition, we see the following error message for some matrices we apply the SVD to:
“Error in : no convergence after 161 steps” and 161 goes up to 175.
What does this imply?
- Are we doing anything wrongly?
- Do we apply the SVD to matrices outside the limitations of the SVD (If yes, which features are the matrices not supposed to have?)?
- Is it a known problem in this version and no longer existing in newer versions?
- Anything other?
Any help will be appreciated.
Eddy Offermann wil answer your questions once he will be back (July 7).
Eddy made several changes to TDecompXXX classes in the past 2 or 3 months. He is also working on the documentation of the linear algebra package.
Could you send the shorted possible test script showing your problem?
We located the problem somewhere else and it does not
The diagonalization algorithm is an iterative process .
If it finds that the off-diagonal elements are still larger
than DBL_EPSILON after the maximum number of iterations
(a number that depends on the matrix size), it will complain .
It is hard to give specifications for the matrices that will
fail (one would hope if we knew them we would check
before proceeding ) . Your matrix must have been
numerically very ill-conditioned .
The steps taken in the SVD algorithm have been described
in great detail in
Data Analysis, “Statistical and Computational Methods for Scientists and Engineers” , by Siegmund Brandt .