Rachit Prasad <[email protected]> writes: > Hello, > > I am working to solve a linear system of equations using a KSP solver. The > size of the matrix is approximately ~370,000. The following settings have > been used to set up the solver" > > [image: Inline image 1] > > The L2 norm of the RHS for this problem is ~365, which has been calculated > using the subroutine *VecNorm*. When I solve this system, I am finding that > irrespective of the number of maximum iterations the L2 norm of final > residual is pretty much constant around ~37860.
Your solver has stagnated, either due to numerical precision issues (e.g., if the matrix entries are enormous in this case) or (more likely) due to an inadequate/unstable preconditioner. > The L2 norm of the residual has been found using the subroutine > *KSPGetResidualNorm. *The following is how the residual is varying for > different maximum iterations. *[image: Inline image 2]* > > With such being the case, I have the following doubts to clarify: > > - Are the settings used by me for the KSP solver adequate enough? Or am > I missing something? > - What could be reasons for my KSP solver to not converge below the > permissible tolerance? > - For a linear system of given size, how can one determine the number of > maximum iterations that should be set for the KSP solver? Is there a thumb > rule? > > > Regards, > Rachit Prasad
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