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VICTOR_K_Intel

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04-10-2014
10:04 PM

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Explicit SVD vs SVD with eigen solver

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.

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