I have been talking with Boris Kaus and Patrick Sanan about trying to revive
the Julia PETSc interface wrappers. One of the first things to get going is to
use Julia's binary builder [1] to wrap more scalar, real, and int type builds
of the PETSc library; the current distribution is just Real,
Sounds good. Yes the outer most Jacobian can be shell (or even nest but then
I think you need to "build" it yourself, I don't think the DMDA will give back
an appropriate nest matrix.)
> On Jul 1, 2021, at 4:10 PM, Matteo Semplice
> wrote:
>
> Thank you, Matthew and Barry!
>
> I can now
Thank you, Matthew and Barry!
I can now see a way forward.
Il 01/07/21 21:42, Barry Smith ha scritto:
I do not understand how creating a DMDA with n0+n1 dofs will let me easily
reuse my shell preconditioner code on the top-left block.
PCFIELDSPLIT (and friends) do not order the dof by
> On Jul 1, 2021, at 11:44 AM, Matteo Semplice
> wrote:
>
> Il 01/07/21 17:52, Jed Brown ha scritto:
>> I think ex28 is better organization of code. You can DMCreateMatrix() and
>> then set types/preallocation for off-diagonal blocks of the MatNest. I think
>> the comment is unclear and
On Thu, Jul 1, 2021 at 11:44 AM Matteo Semplice <
matteo.sempl...@uninsubria.it> wrote:
> Il 01/07/21 17:52, Jed Brown ha scritto:
> > I think ex28 is better organization of code. You can DMCreateMatrix()
> and then set types/preallocation for off-diagonal blocks of the MatNest. I
> think the
Il 01/07/21 17:52, Jed Brown ha scritto:
I think ex28 is better organization of code. You can DMCreateMatrix() and then
set types/preallocation for off-diagonal blocks of the MatNest. I think the
comment is unclear and not quite what was intended and originally worked (which
was to assemble
Matteo Semplice writes:
> Hi.
>
> We are designing a PETSc application that will employ a SNES solver on a
> multiphysics problem whose jacobian will have a 2x2 block form, say
> A=[A00,A01;A10,A11]. We already have code for the top left block A_00 (a
> MatShell and a related Shell
Peder,
PETSCSF_PATTERN_ALLTOALL only supports MPI_Alltoall (not Alltoallv), and
is only used by petsc internally at few places.
I suggest you can go with Matt's approach. After it solves your problem,
you can distill an example to demo the communication pattern. Then we can
see how to
Peder Jørgensgaard Olesen writes:
> Each process is assigned an indexed subset of the tasks (the tasks are of
> constant size), and, for each task index, the relevant data is scattered as a
> SEQVEC to the process (this is done for all processes in each step, using an
> adaption of the code
Dear Jed
I'm not really sure what it is you're asking (that's on me, still a rookie in
the field), but I'll try to describe what I've done:
Each process is assigned an indexed subset of the tasks (the tasks are of
constant size), and, for each task index, the relevant data is scattered as a
Thank you very much for these suggestions! We are currently using version
3.12, so I'll try to update to the latest version and try your suggestions.
Let me get back to you, thanks!
On Thu, Jul 1, 2021, 4:45 AM Jose E. Roman wrote:
> Then I would try Davidson methods
Then I would try Davidson methods https://doi.org/10.1145/2543696
You can also try Krylov-Schur with "inexact" shift-and-invert, for instance,
with preconditioned BiCGStab or GMRES, see section 3.4.1 of the users manual.
In both cases, you have to pass matrix A in the call to EPSSetOperators()
Thanks. I actually do have a 1st order approximation of matrix A, that I
can explicitly compute and also invert. Can I use that matrix as
preconditioner to speed things up? Is there some example that explains how
to setup and call SLEPc for this scenario?
On Thu, Jul 1, 2021, 4:29 AM Jose E.
For smallest real parts one could adapt ex34.c, but it is going to be costly
https://slepc.upv.es/documentation/current/src/eps/tutorials/ex36.c.html
Also, if eigenvalues are clustered around the origin, convergence may still be
very slow.
It is a tough problem, unless you are able to compute a
I'm solving for the smallest eigenvalues in magnitude. Though is it cheaper
to solve smallest in real part, as that might also work in my case? Thanks
for your help.
On Thu, Jul 1, 2021, 4:08 AM Jose E. Roman wrote:
> Smallest eigenvalue in magnitude or real part?
>
>
> > El 1 jul 2021, a las
Smallest eigenvalue in magnitude or real part?
> El 1 jul 2021, a las 11:58, Varun Hiremath escribió:
>
> Sorry, no both A and B are general sparse matrices (non-hermitian). So is
> there anything else I could try?
>
> On Thu, Jul 1, 2021 at 2:43 AM Jose E. Roman wrote:
> Is the problem
Sorry, no both A and B are general sparse matrices (non-hermitian). So is
there anything else I could try?
On Thu, Jul 1, 2021 at 2:43 AM Jose E. Roman wrote:
> Is the problem symmetric (GHEP)? In that case, you can try LOBPCG on the
> pair (A,B). But this will likely be slow as well, unless
Is the problem symmetric (GHEP)? In that case, you can try LOBPCG on the pair
(A,B). But this will likely be slow as well, unless you can provide a good
preconditioner.
Jose
> El 1 jul 2021, a las 11:37, Varun Hiremath escribió:
>
> Hi All,
>
> I am trying to compute the smallest
Hi All,
I am trying to compute the smallest eigenvalues of a generalized system
A*x= lambda*B*x. I don't explicitly know the matrix A (so I am using a
shell matrix with a custom matmult function) however, the matrix B is
explicitly known so I compute inv(B)*A within the shell matrix and solve
Hi.
We are designing a PETSc application that will employ a SNES solver on a
multiphysics problem whose jacobian will have a 2x2 block form, say
A=[A00,A01;A10,A11]. We already have code for the top left block A_00 (a
MatShell and a related Shell preconditioner) that we wish to reuse. We
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