Good day,
is there an documentation on how the svd decomposition is implemented? I'm not sure if I really need to compute the characteristic polynomial of A^T A then solve for homogeneous equation for a specific matrix. It seems too messy and not optimal.
thanks in advance
OpenCV is not specifically designed for SVD operation, so you can't get a fast speed with even a quite small matrix, i.e. 500x500. Surprisingly, the results of OpenCV's SVD are not the identical to that of Matlab on the same input matrix. Hence, if your wish is finding an efficient library to use its SVD function, my suggestion is lapack (the best one), Eigen and Armadillo.
AFAIK you can build openCV with Eigen support resulting in some functions using the optimized version of that library. That should solve the slower implementation of OpenCV itself.
Yes, thanks @StevenPuttemans, I usually forget that detail.
Good day, thanks for the quick response. Im not actially looking for an svd library. Im actually trying to code an svd algorithm from scratch as an excercise. Any suggestions where i can start?
Hi all,
does anybody know what algorithm is implemented?
If I'm not mistaken, the actual implementation is in method JacobiSVDImpl_ (modules/core/src/lapack.cpp).
Googling Jacobi & SVD should do the trick, but there is a plethora or results.
I guess the code is based on some paper/book. Any suggestions?
Thank you.
Asked: 2014-06-04 10:53:09 -0600
Seen: 1,430 times
Last updated: Nov 18 '14
