![]() = ndgrid(linspace(-pi,pi,N),linspace(-pi,pi,N)) If you want to play around with this example, I've included the MATLAB code I used below. Doing so for a simple case with a known solution gives the following error versus iteration number for these two methods:Īs you can see, SOR reaches machine precision in about 100 iterations at which point Gauss-Seidel is about 25 orders of magnitude worse. Where $\Delta x$ is the grid spacing and $L$ is the domain size. ![]() ![]() I was experimenting with SOR for matrix of this king: I would like something much simpler - just few examples of matrices (problems) for which SOR converge faster. I don't see simple heuristics how to estimate spectral radius just looking on the matrix ( or problem which it represents ). I mean just by looking on the matrix, or knowledge of particular problem the matrix represents?Īre there any heuristics for optimizing the successive over-relaxation (SOR) method?īut it's a bit too sophisticated. Is there any simple rule of thumb to say if it is worth to do SOR instead of Gauss-Seidel? ( and possible way how to estimate realxation parameter $\omega$)
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |