Just because using mumps gives me strange results..
2014-07-22 13:18 GMT+02:00 Jan Blechta <[email protected]>: > [please, keep [email protected] in CC] > > On Tue, 22 Jul 2014 12:15:45 +0200 > Maria Cristina Colombo <[email protected]> wrote: > > > Is there a way to continue using > > > problem = MyNonlinearProblem(L,a,bc) > > > solver = NewtonSolver() > > > solver.parameters["linear_solver"] = "lu" > > > solver.parameters["convergence_criterion"] = "incremental" > > > solver.parameters["relative_tolerance"] = 1e-6 > > > > without having that problem? > > Well, I don't know. My point was that LU solvers usually tend to having > problems when the underlying equations are stiff/difficult to > solve (well-posedness is close to be lost). So you could try tweaking > parameters of your problem (including spatial/time resolution, > time-stepping scheme, regularization parameters if any...) to make the > problem more numerically stable for LU factorization. > > Nevertheless, I would recommend you trying > solver.parameters["linear_solver"] = "mumps" > I don't see a reason why this is unacceptable for you? > > Jan > > > I'm not understanding what you say :( > > > > > > 2014-07-22 11:51 GMT+02:00 Jan Blechta <[email protected]>: > > > > > On Tue, 22 Jul 2014 11:19:58 +0200 > > > Maria Cristina Colombo <[email protected]> wrote: > > > > > > > Dear all, > > > > > > > > I'm trying to solve a nonlinear problem on a very big mesh using > > > > newton solver. These are the lines of my code: > > > > problem = MyNonlinearProblem(L,a,bc) > > > > solver = NewtonSolver() > > > > solver.parameters["linear_solver"] = "lu" > > > > solver.parameters["convergence_criterion"] = "incremental" > > > > solver.parameters["relative_tolerance"] = 1e-6 > > > > > > > > I encountered this error: > > > > > > > > UMFPACK V5.4.0 (May 20, 2009): ERROR: out of memory > > > > > > > > Traceback (most recent call last): > > > > File "CH_BC_Tdip.py", line 170, in > > > > solver.solve(problem, u.vector()) > > > > RuntimeError: > > > > > > > > *** > > > > > ------------------------------------------------------------------------- > > > > *** DOLFIN encountered an error. If you are not able to resolve > > > > this issue *** using the information listed below, you can ask > > > > for help at ------------------------------ > > > > > > > > *** [email protected] > > > > ------------------------------ > > > > > > > > *** Remember to include the error message listed below and, if > > > > possible, *** include a *minimal* running example to reproduce the > > > > error. ------------------------------ > > > > > > > > *** > > > > > ------------------------------------------------------------------------- > > > > *** Error: Unable to successfully call PETSc function 'KSPSolve'. > > > > *** Reason: PETSc error code is: 76. > > > > *** Where: This error was encountered inside > > > > /build/buildd/dolfin-1.4.0+dfsg/dolfin/la/PETScLUSolver.cpp. > > > > *** Process: unknown > > > > > > > > > > > > How can I fix the problem? I have found out that I should switch > > > > to mumps as linear solver.. but I prefer to use LU. Is there a > > > > way to > > > > > > MUMPS is also LU/Cholesky solver. > > > > > > > save memory? Is it related to the 4GB limit of UMFPACK? I'm new at > > > > dolfin and I don't know how to solve my model .. > > > > > > I don't know much about UMFPACK but in LU solver you usually get run > > > out of memory when problem is stiff and too much of pivoting is > > > required for accuracy so fill-in is large. Try fixing the stiffness > > > of your problem. > > > > > > I'd recommend using MUMPS when one can set plenty of MUMPS options > > > (see MUMPS manual) from DOLFIN by > > > > > > PETScOptions.set("mat_mumps_icntl_foo", bar) > > > PETScOptions.set("mat_mumps_cntl_foo", bar) > > > > > > But generally, one should switch to Cholesky or even > > > positive-definite Cholesky when the problem is symmetric or SPD > > > respectively. The theory of factorization is much more stronger > > > there and solvers' robustness reflect that. > > > > > > Jan > > > > > > > > > > > > > > > Thanks > > > > > > > > Cristina > > > > > > > >
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