Hi,
could you clarify two following questions.
- Consider following mathematical problem. Given general full rank square non symmetric matrix A of 13 000 x 13 000 size I want to find its SVD with all singular values and all right/left eigen vectors. But when I solve it with some driver SVD routine (e.g. LAPACKE_sgesdd) it takes about 2 x slower then I solve two eigen decomposition problems at a time: for A*A' (getting left eigen vectors of A) and A'*A (getting right eigen vectors of A) matrices.
Is it normal behavior or I miss something and there is more proper/fast way to find SVD for given matrix type explicitly (via SVD driver routine)?
- Considering the same problem, is there any way in MKL to find small subset (let it be 1 left and right eigen vectors for example) of all SVD right/left eigen vectors with the biggest singular values, saving "considerable" amount of time (at least 30%) ?
Thank you in advance.
