[petsc-users] Optimization methods in PETSc/TAO

[Justin Chang]

This was one of the citations provided:

M. Ulbrich, "Semismooth Newton Methods for Variational
Inequalities and Constrained Optimization Problems in
Function Spaces", SIAM, 2011,

Haven't looked into this in detail, but is what's described in that
equivalent to the SNESVINEWTONSSLS?

On Fri, Jan 22, 2016 at 3:42 PM, Matthew Knepley <knepley at gmail.com> wrote:

> On Fri, Jan 22, 2016 at 4:27 PM, Justin Chang <jychang48 at gmail.com> wrote:
>
>> Hi all,
>>
>> Consider the following problem:
>>
>> minimize  1/2<c,Kc> - <c,f>
>> subject to  c >= 0                     (P1)
>>
>> To solve (P1) using TAO, I recall that there were two recommended solvers
>> to use: TRON and BLMVM
>>
>> I recently got reviews for this paper of mine that uses BLMVM and got
>> hammered for this, as I quote, "convenient yet inadequate choice" of
>> solver.
>>
>
> If they did not back this up with a citation it is just empty snobbery,
> not surprising from some quarters.
>
>
>> It was suggested that I use either semi smooth Newton methods or
>> projected Newton methods for the optimization problem. My question is, are
>> these methodologies/solvers available currently within PETSc/TAO?
>>
>
> You can Google TRON and BLMVM and they come up on the NEOS pages. BLMVM is
> a gradient descent method, but
> TRON is a Newton method, so trying it may silence the doubters.
>
>   Matt
>
>
>> 1) I see that we have SNESVINEWTONSSLS, and I tried this over half a year
>> ago but it didn't seem to work. I believe I was told by one of the PETSc
>> developers (Matt?) that this was not the one to use?
>>
>> 2) Is TRON a type of projected Newton method? I know it's an active-set
>> Newton trust region, but is this a well-accepted high performing
>> optimization method to use?
>>
>> I was also referred to ROL: https://trilinos.org/packages/rol but I am
>> guessing this isn't accessible/downloadable from petsc at the moment?
>>
>> Thanks,
>> Justin
>>
>
>
>
> --
> 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
>
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