Strategy for finding optimal omega in SOR method
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I had written an algorithm that searches for the optimal weight parameter to be implemented in the successive-over relaxation (SOR) method which worked cleanly by vectorizing the interval
and for each ω the spectral radius of the iteration matrix is computed.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/711557/image.png)
However, I was advised not to use this approach for large sparse matrices as it is expensive to compute (the same way computing condition number of a large matrix is unfeasible) and rather use it as a demonstration tool. Therefore, I was wondering what strategy is the best to approximate the optimal weight parameter for large sparse systems (
) that would allow the best convergence of the SOR.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/711562/image.png)
Furthermore, as a result of my question I was wondering if classical iterative stationary methods such as Jacobi, Gauss-Seidel, and the SOR are worthy to be used nowadays in dealing with large sparse systems or is the default preference Krylov methods?
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