On Tue, 22 Jul 2014 13:35:38 +0200 Maria Cristina Colombo <[email protected]> wrote:
> Just because using mumps gives me strange results.. As a rule of thumb, if Newton solver converges with MUMPS, the problem is not in MUMPS but on your side in the specification of the problem being solved. Jan > > > 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 > > > > > > > > > > > > _______________________________________________ fenics mailing list [email protected] http://fenicsproject.org/mailman/listinfo/fenics
