[Ipopt] improving IPOPT speed with Algorithmic DifferentiationTheory

[Brad Bell]

Are you using algorithmic differentiation to compute your derivatives 
for Ipopt ? I have been working on an interface to do this. See
http://www.coin-or.org/CppAD/Doc/ipopt_cppad_nlp.xml

Sebastian Walter wrote:
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> Hello everyone,
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> We are working on a software project called VPLAN which computes optimal
> experimental designs.
>
> At the moment we use SNOPT for the optimization.  However, SNOPT is
> proprietary and therefore we are looking for good alternatives ;)
>
> We have already successfully incorporated IPOPT. The optimization works
> and gives the same results as SNOPT.
>
> However, for our test examples, SNOPT clearly outperforms IPOPT w.r.t
> function evaluations until convergence.
>
> Well, so we'd like to speed up IPOPT a little bit.
>
> We noticed that often the following happens in IPOPT
> ...
> eval_f(x_13)
> eval_grad_f(x_13)
> eval_f(x_14)
> eval_grad_f(x_14)
> eval_f(x_15)
> eval_grad_f(x_15)
>
> The Algorithmic Differentation theory tells us, that we get the function
> for free when we evaluate the gradient.
> All we need is some possibility to cache the redundant computations.
>
> Is there an easy way to do that in IPOPT?
>
>
> best regards,
> Sebastian Walter
>
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