On 6/3/24 02:03, Najwa Alshehri wrote:
Thank you for bringing this to my attention. I would definitely love to share
the code in the code gallery for the benefit of others. In this case, I will
create a code for a simpler problem, such as the Stokes problem or mixed
Laplace problem, to illustra
Wolfgang:
Thank you for bringing this to my attention. I would definitely love to
share the code in the code gallery for the benefit of others. In this case,
I will create a code for a simpler problem, such as the Stokes problem or
mixed Laplace problem, to illustrate the concept of the inf-sup
Najwa:
on a separate note: Computing the inf-sup constant is an interesting problem.
It would be nice to have a program that shows how to do that available as the
base for others' experiments. Could I interest you in submitting your code,
once you've got it working, to the code gallery?
ht
Dear Luca,
Thank you for your answer, that works perfectly.
Best,
Najwa
On Sunday, June 2, 2024 at 12:27:54 PM UTC+3 luca@gmail.com wrote:
> To be more specific, you can use inverse_operator, to which you pass A, a
> solver, and a preconditioner, and it returns an operator the applies the
To be more specific, you can use inverse_operator, to which you pass A, a solver, and a preconditioner, and it returns an operator the applies the inverse of A to a vector. Just as your mass inverse.LucaIl giorno 2 giu 2024, alle ore 11:25, Luca Heltai ha scritto:Dear Najwa, You don’t need to cre
Dear Najwa, You don’t need to create the inverse of A. Just its action, pretty much in the exact same way you did for the mass matrix, but replacing the inverse operator done with umfpack with one done with CG. LucaIl giorno 2 giu 2024, alle ore 09:42, Najwa Alshehri ha scritto:Wolfgang and all,I
Wolfgang and all,
I have a positive definite matrix M, and its inverse can be quickly and
easily found in deal.II using the following lines:
M_inv_umfpack.initialize(M);
auto M = linear_operator(M);
auto M_inv = linear_operator(M, M_inv_umfpack);
However, when solving the direct system, I need
On 6/1/24 14:49, Najwa Alshehri wrote:
I decided to solve the exact problem directly, namely AA x = \lambda M x. To
achieve this, I computed the inverse of the matrix AA= Bt * A^inv * B using a
Conjugate Gradient (CG) solver. Subsequently, I solved for the exact
eigenvalues, and to my satisf
Hello all,
One more time, thanks for your support. I've *managed to identify and
resolve the issue I was facing*, and I'd like to share the solution here
for the benefit of others who might encounter similar challenges.
The problem stemmed from my approach of solving for the reciprocals of the
Dear Wolfgang:
Thank you for your answer. I have checked the matrices and I printed the
case for a 2 by 2 mesh from both Matlab and deal.II. With a simple change
of rows and columns, they are exactly the same ( see the files attached).
Not sure yet why they result in different results of the ei
Do you use the same "mass matrix" in both cases, meaning do you call
eig(A, M) where A is the stiffness matrix and M is the mass matrix in
matlab?
You will need to compute the spectrum of the eigenvalue problem
A x = \lambda M x
Best,
Matthias
On Thu, May 30, 2024, at 10:55 CDT, Najwa Alsh
On 5/30/24 09:55, Najwa Alshehri wrote:
The problem arises when comparing the inf-sup constants obtained using Matlab
and dealii (solving on the same mesh ):
1. Matlab: Using the "eig" solver, the inf-sup constant decays to zero as the
mesh is refined.
2. deal.II: Using the "ArpackSolve
Dear all,
Thank you for your always support and help.
I am encountering an issue while solving an eigenvalue problem related to
the computation of the discrete inf-sup constant for a saddle point
problem. Specifically, I am solving the following system:
Bt A^-1 B eigenvector = eigenvalue
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