Hi,
I saw some discussion in the mailing list, while not a lot.
I am wondering the current status about using PETSc with MATLAB before
I dig in.
To be short, Would I be able to use the non-linear solver or linear
solver with a MATLAB code?
For example, I have a simulation code with MATLAB, I wa
Hi all,
I might be missing something obvious, but I can't tell from the documentation
if 1D coordinate DMSWARMs (immersed in interval DMPlex meshes) are supported.
Does anyone know?
I've successfully implemented 2D and 3D coordinate DMSwarms in 2D and 3D
DMPlexes using DMSwarmSetPointCoordinat
On Tue, Apr 14, 2020 at 5:49 AM Hill, Reuben
wrote:
> Hi all,
>
> I might be missing something obvious, but I can't tell from the
> documentation if 1D coordinate DMSWARMs (immersed in interval DMPlex
> meshes) are supported. Does anyone know?
>
> I've successfully implemented 2D and 3D coordinat
Hi Jed/PETSc-developers,
My goal is to invert a set of these PDE's to obtain a series of parameters
F_t (with TSSolve and TSAdjoint for function/gradient computation). I was
planning to use TAO for setting up the inverse problem but given that TAO
doesn't support complex scalars, I'm re-thinking a
Hi Junchao,
We have tried your two suggestions but the problem remains.
And the problem seems to be on the MPI_Isend line 117 in
PetscGatherMessageLengths and not MPI_AllReduce.
We have now tried Intel MPI, Mpich, and OpenMPI, and so are thinking the
problem must be elsewhere and not MPI.
Give
On Mar 27, 2019, at 8:07 PM, Sajid Ali via petsc-users
mailto:petsc-users@mcs.anl.gov>> wrote:
Hi,
I'm able to solve the following equation using complex numbers (with ts_type cn
and pc_type gamg) :
u_t = A*u'' + F_t*u;
(where A = -1j/(2k) amd u'' refers to u_xx+
Hi Hong,
Apologies for creating unnecessary confusion by continuing the old thread
instead of creating a new one.
While I looked into converting the complex PDE formulation to a real valued
formulation in the past hoping for better performance, my concern now is
with TAO being incompatible with c
On Tue, Apr 14, 2020 at 2:44 PM Sajid Ali
wrote:
> Hi Hong,
>
> Apologies for creating unnecessary confusion by continuing the old thread
> instead of creating a new one.
>
> While I looked into converting the complex PDE formulation to a real
> valued formulation in the past hoping for better pe
There is an MPI_Allreduce in PetscGatherNumberOfMessages, that is why I
doubted it was the problem. Even if users configure petsc with 64-bit
indices, we use PetscMPIInt in MPI calls. So it is not a problem.
Try -vecscatter_type mpi1 to restore to the original VecScatter
implementation. If the prob
Hi Matthew,
The TAO manual states that (preface, page vi) "However, TAO is not
compatible with PETSc installations using complex data types." (The tao
examples all require !complex builds. When I tried to run them with a petsc
build with +complex the compiler complains of incompatible pointer type
Tao does not support --with-scalar-type=complex
Il Mar 14 Apr 2020, 22:09 Matthew Knepley ha scritto:
> On Tue, Apr 14, 2020 at 2:44 PM Sajid Ali <
> sajidsyed2...@u.northwestern.edu> wrote:
>
>> Hi Hong,
>>
>> Apologies for creating unnecessary confusion by continuing the old thread
>> instead
First, you need to order your equations (r_0, i_0, r_1, i_1, ...) and then
set a block size of two (times the real block size of your equations) in
the matrix, for GAMG to work. PETSc can do this for you with fieldsplit.
The symmetric stuff that GAMG requaries is just for the (parallel) graph
coar
Sorry for the time travel. As far as I know, optimization over complex-valued
parameters is not a well-defined problem. I am not sure how you can develop an
optimization algorithm for it. Perhaps our optimization experts have better
suggestions in this direction.
The real-valued formulation see
Not true in general when you minimize an objective function as a functional of
the parameter only
For same methods (Newton for example, gradient descent, etc) the state
variables do no enter the minimization, so it should be fine to have
complex-valued state variables
> On Apr 15, 2020, at 1:0
On Tue, Apr 14, 2020 at 6:26 PM Stefano Zampini
wrote:
> Not true in general when you minimize an objective function as a
> functional of the parameter only
> For same methods (Newton for example, gradient descent, etc) the state
> variables do no enter the minimization, so it should be fine to h
This is correct. As long as the optimization variables and the objective
function, and it’s gradient are real valued, intermediate variables (such as
PDE states) can be complex.
In principle it is also possible to minimize real valued functions in complex
variables by converting to rectangular
We'd have complex values in vectors that contain the likes of gradients
with respect to (real-valued) parameters so there would likely need to
be lots of PetscRealPart() within TAO. It won't just compile if we turn
on complex, but these changes should be feasible and is surely a better
solution th
In Sajid’s problem, the optimization variables (F_t in the equation u_t =
A*(u_xx + u_yy) + F_t*u) are complex-valued. The gradients should also be
complex-valued. The objective function may be real-valued.
Hong (Mr.)
On Apr 14, 2020, at 5:52 PM, Dener, Alp mailto:ade...@anl.gov>>
wrote:
This
Thanks for your example. My problem is resolved. Meanwhile I am wondering, if
it is possible to make this example more flexible. I mean what if the columns
in each row are different? Is there any way to insert them all together?
Regards
Bin
From: Junchao Zhang [mailto:junchao.zh...@gmail.com]
S
Thanks Jacob. In my problem the rows/columns are not contiguous. Your
suggestions are also very good. I will think about how to make use it somewhere
else in my code.
Best
Bin
From: Jacob Faibussowitsch [mailto:jacob@gmail.com]
Sent: Monday, 13 April 2020 11:47 PM
To: Junchao Zhang
Cc: Bin
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