Hello all,
I am wondering how to solve such eigen value problem with FreeFEM (in which the matrices are not associated with a finite element operator).
In this case the first and second matrices are spare matrices and the Phi_i are FE functions.
When I applied the classical method:
int l = EigenValue(OP, B, sym=true, sigma=sigma, value=ev, vector=eV, tol=1e-10, maxit=0, ncv=0) I get a segmentation fault.
Thank you in advance for your help,
Best regards,
Loïc,
It’s not quite clear what you need here, but let me try to help anyways.
First, I’m not sure if this is part of your questions, but you can create (or load) a sparse matrix without using FE functions.
Second, if the Psi are not FE functions, you may need to use array= instead of vector= when you call EigenValue.
Hope that helps!
Hello Chris,
Thank you for your reply,
Yes the Psi_i are FE function. Basically they are some basis functions that I have computed on a fine mesh inside my coarse element.
And when I declared my eigenvalue as array, I get
Impossible to cast in <13FEbaseArrayKnIdE>.
Best regards,
Loïc
Can you send your freefem script, because your problem is not clear for me!
BASIS_TEST.zip (803.7 KB)
Dear Frédéric,
Thank you for your help, I have prepared a MWE of my problem.
You can find it in attachment, here:
MWE_EigenValue_Problem.edp (2.4 KB)
Best regards,
Loïc,
The script corrected
MWE_EigenValue_Problem.edp (2.5 KB)
Thanks for your help !
Best regards,
Loïc,
Hello all,
With this correction
MWE_EigenValue_Problem_Correct.edp (2.5 KB)
(with the same set of basis functions previously attached), for each element, the FreeFEM code is able to find 3 eigenvalues (whose values are 0 and 48 and 120).
However, when I do the calculation with python using the same matrices, we find 4 eigenvalues : the three found by FreeFEM (0 and 48 and 120) + another one (whose value is 240).
Why FreeFEM does not find this fourth eigenvalue ?
Thank you in advance for your help,
Best regards,
Loïc,
The EigenValue function uses ARPACK, which is not designed to compute the complete eigenspectrum of a matrix. It is generally used for computing only a few eigenvalues/eigenvectors from large sparse matrix systems.
If you change the shift to a value closer to 240, you should find that eigenvalue (though in this case you won’t see the zero eigenvalue.)
Hello,
Thank you for your reply,
However is there a way to compute more eigenvalues (if we do not know a priori the following eigenvalues) ?
Best regards,
Loïc,
If the matrix is not to big (<1000) you can use lapack!
Thanks !
But I cannot find the same values with the function dggev of lapack.
It works fine with the function EPSSolve of PETSc. However I wonder if it exists a parameter to compute the eigenvalue from the smallest to the largest ?
Best regards,
Loïc,
-eps_type lapack
is the only choice if you need the full spectrum.
