LOBPCG method for Eigen Value Computation.

[Nithishify]

Hi,

I am new to MAGMA. I want to find the top K(K is a very small number) eigen vectors of a sparse symmetric matrix whose dimension is more than 30000. In addition, the matrix in consideration is positive semi definite. However, I am not able to understand the arguments that needs to be passed to the magma_slobpcg function for the magma_s_solver_par and the magma_s_preconditioner structures. I wanted to know the parameters that I have to set as a minimum requirement. Also, what parameters that i could enable to perform the calculations faster?

Thanks in advance,
Nithish

[hartwig anzt]

Dear Nithish,

for a plain LOBPCG, use ./testing_dsolver --solver 8 --ev k /path/to/the/matrix

If you want to use a preconditioner (faster solver convergence in many cases) type

./testing_dsolver --solver 8 --ev k --precond x /path/to/the/matrix

where x = 1 is Jacobi, x = 2 is ILU.

Let me know whether this helps!

Thanks, Hartwig

[Nithishify]

Hi Hartwig,

Thanks for the reply. I tried the use the solver using the method suggested by you and it helped me to find the eigenvalues correctly. However instead of providing the k-largest eigen values, it provides me with K-smallest ones. Is there a setting for the algorithm which calculates the largest eigen values?

With regards,
Nithish

[Stan Tomov]

Nithish,
We haven't provided explicitly an option for that but if you compute the eigenvalues for -A, and multiply the result by -1 at the end, you will get the set of the largest eigenvalues for A.
Best regards,
Stan

[Nithishify]

Thanks for the info, Stan