[petsc-users] Converting complex PDE to real for KNL performance ?

[Stefano Zampini]

Tao does not support --with-scalar-type=complex

Il Mar 14 Apr 2020, 22:09 Matthew Knepley <knepley at gmail.com> ha scritto:

> On Tue, Apr 14, 2020 at 2:44 PM Sajid Ali <
> sajidsyed2021 at u.northwestern.edu> 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 performance, my concern
>> now is with TAO being incompatible with complex scalars. I would've
>> preferred to keep the complex PDE formulation as is (given that I spent
>> some time tuning it and it works well now) for cost function and gradient
>> evaluation while using TAO for the outer optimization loop.
>>
>> Using TAO has the obvious benefit of defining a multi objective cost
>> function, parametrized as a fit to a series of measurements and a set of
>> regularizers while not having to explicitly worry about differentiating the
>> regularizer or have to think about implementing a good optimization scheme.
>> But if it converting the complex formulation to a real formulation would
>> mean a loss of well conditioned forward solve (and increase in solving time
>> itself), I was wondering if it would be better to keep the complex PDE
>> formulation and write an optimization loop in PETSc while defining the
>> regularizer via a cost integrand.
>>
>>
> What exactly is the problem with TAO and complex? Is it only for some
> methods?
>
>   Thanks,
>
>     Matt
>
>
>> Thank You,
>> Sajid Ali | PhD Candidate
>> Applied Physics
>> Northwestern University
>> s-sajid-ali.github.io
>>
>
>
> --
> What most experimenters take for granted before they begin their
> experiments is infinitely more interesting than any results to which their
> experiments lead.
> -- Norbert Wiener
>
> https://www.cse.buffalo.edu/~knepley/
> <http://www.cse.buffalo.edu/~knepley/>
>
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