<div dir="ltr">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.<div><br></div><div>The symmetric stuff that GAMG requaries is just for the (parallel) graph coarsening and you just need to add a parameter where GAMG will symmetrize the graph used for coarsening, not your real matrix. Or you can use a zero threshold.</div><div><br></div><div>Chebyshev is the default smoother in GAMG. Chebyshev is not well suited to asymmetric matrices. You need to use the right form, which you seem to have a handle on, and if the asymmetry is not too bad cheby might work. Otherwise, I would use gmres or richardson/jacobi with a proper damping parameter. If you use gmres you want to use fgmres as the outer solver.</div><div><br></div><div>Good luck, this is a tricky business,</div><div>Mark</div><div><br></div><div><br></div><div><br></div></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Tue, Apr 14, 2020 at 3:33 PM Stefano Zampini <<a href="mailto:stefano.zampini@gmail.com">stefano.zampini@gmail.com</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="auto">Tao does not support --with-scalar-type=complex</div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">Il Mar 14 Apr 2020, 22:09 Matthew Knepley <<a href="mailto:knepley@gmail.com" target="_blank">knepley@gmail.com</a>> ha scritto:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div dir="ltr">On Tue, Apr 14, 2020 at 2:44 PM Sajid Ali <<a href="mailto:sajidsyed2021@u.northwestern.edu" rel="noreferrer" target="_blank">sajidsyed2021@u.northwestern.edu</a>> wrote:<br></div><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div><div dir="ltr"><div>Hi Hong, <br><br></div>Apologies for creating unnecessary confusion by continuing the old thread instead of creating a new one. <br><br></div><div>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. <br><br></div><div>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.<br></div><br></div></div></blockquote><div><br></div><div>What exactly is the problem with TAO and complex? Is it only for some methods?</div><div><br></div><div> Thanks,</div><div><br></div><div> Matt</div><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Thank You, <br><div><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div style="font-size:12.8px">Sajid Ali | PhD Candidate<br></div><div style="font-size:12.8px">Applied Physics<br></div><div style="font-size:12.8px">Northwestern University</div><div style="font-size:12.8px"><a href="http://s-sajid-ali.github.io" rel="noreferrer" target="_blank">s-sajid-ali.github.io</a></div></div></div></div></div></div></div></div></div>
</blockquote></div><br clear="all"><div><br></div>-- <br><div dir="ltr"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div>What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead.<br>-- Norbert Wiener</div><div><br></div><div><a href="http://www.cse.buffalo.edu/~knepley/" rel="noreferrer" target="_blank">https://www.cse.buffalo.edu/~knepley/</a><br></div></div></div></div></div></div></div></div>
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