[Trilinos-Users] Recycling Conjugate Gradient (RCG) for poisson problem

[Bart Janssens]

Hello,

I need to solve a series of Poisson problems, where the matrix stays
exactly the same every timestep but the RHS changes. I'm not sure what
the best approach is, I've tried pre-factoring the matrix using MUMPS,
but this consumes way too much memory so it won't scale beyond 1.5M
mesh nodes on our cluster.

I thought maybe the recycling methods might help, but I'm not sure how
well these are supposed to work on a problem like this. In Trilinos
11.0.3 (and 10.12 before), the RCG method seems to give strange
results: a Belos BlockCG solve preconditioned with ML runs in about 90
CG iterations, but when keeping all settings the same except for
changing to RCG, it won't converge anymore (stops at 1000 iterations,
even on the first solve).

Does anyone have any pointers for using the RCG method, or other ideas
of how I could accelerate the convergence for this problem, possibly
exploiting the fact that the matrix stays the same for the entire
simulation? The poisson problem is for a scalar on a 3D hexahedral
finite element mesh.

Kind regards,

-- 
Bart