routines for nonsymmetric stiffness matrix with skyline storage
routines for nonsymmetric stiffness matrix with skyline storage
(OP)
I'm developing finite element program myself for the research. We used the skyline storage for the system matrix. Now the new stiffness matrix is unsymmetric and all the solver I can find is for symmetric matrix. Could anyone tell me where I can find the program with skyline storage scheme to solve the unsymmetric matrix? Thank you very much for your help!!!!





RE: routines for nonsymmetric stiffness matrix with skyline storage
Is that what you need?
RE: routines for nonsymmetric stiffness matrix with skyline storage
RE: routines for nonsymmetric stiffness matrix with skyline storage
Between ways to store sparse matrix I think that sky line is the most suitable for direct Gauss - like elimination method, so implicitally it should be used for symmetric matrices.
I'm wondering what kind of problem you are solving, since I guess that it shouldn't be a structural neither a diffusion (Poisson like) problem.
For sparse matrices I'd like to advice the use of routines developed at Cornell University at
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If the concern of using a direct method is to check solution existence you can first look for near zero eighenvalue of matrix.
RE: routines for nonsymmetric stiffness matrix with skyline storage
Regards
Dave
RE: routines for nonsymmetric stiffness matrix with skyline storage
RE: routines for nonsymmetric stiffness matrix with skyline storage
RE: routines for nonsymmetric stiffness matrix with skyline storage
RE: routines for nonsymmetric stiffness matrix with skyline storage
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RE: routines for nonsymmetric stiffness matrix with skyline storage
A suitable way to find the solution is to follow numerically what nature does to find a solution: granting that eighenvalues are all non positive (as linear structural or diffusion like problems are) you can find the steady solution after time evolution.
Since the problem is non linear you can check the greater eigenvalue in each linearized time step.
Dx_under_Dt = [A(x,t)] * x - b
x = x + Dx_under_Dt * dt
the steady solution is necessary an x for which
[A(x_,t)]*x_-b=0
[Note: max dt is correlated with greatest eigenvalue.]
You can solve the problem in the above way (Euler's one order method or any other explicit way) even using a Runghe Kutta like (forget the note for stability) or Simpson integration.
I did something similar for a diffusion and advenction (Navier Stokes like) set of equation using Matlab. It could be easly transferred in fortran with sparse allocation and multiplication routines (it took me one month of work for writing PDE problem, coding, debugging).