It seems to give the same results. I exported the matrices to Matlab and checked the estimated condition number of the B matrix which came to ~15 and the 2-norm of the B matrix which was ~10^6. I'm guessing that the large matrix norm is the problem. I glanced over the source for the RQCG solver and it doesn't seem to use a linear solver which is likely why it showed better performance. Do you have any suggestions for dealing with problems like this?
Chris On 02/19/17 12:24, Jose E. Roman wrote: >> El 19 feb 2017, a las 11:00, Christopher Pierce <[email protected]> escribió: >> >> Thanks, >> >> Those changes did improve the tolerances of the solutions. However, I >> still have the same problem. For certain matrices the error is up to >> 10^4 times as large as the requested tolerances and when using true >> residual the solver gets stuck on a certain residual norm the solutions >> and does not converge. I dumped the settings that I used which I'm >> attaching here. >> >> Chris > The settings seem correct. > I would try to solve the problem as a non-symmetric problem: > -eps_gen_non_hermitian > Does it give small residuals? If so, the problem may be that your B-matrix is > quite bad (high norm or ill-conditioned). > > Jose > >
