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From Ajo Fod <ajo....@gmail.com>
Subject Re: [math] Using the hessian in scalar unconstrained optimization
Date Fri, 16 Aug 2013 18:17:59 GMT
If you're talking about analysis.differentiation package. I'm looking
forward to using it.

Though, my main point in this thread was that if one can compute the
hessian, it should be possible in certain cases to speed up convergence
rather than depending on an update rule to estimate an *approximate*
hessian in the Newton step.

Cheers,
-Ajo


On Fri, Aug 16, 2013 at 10:03 AM, Luc Maisonobe <Luc.Maisonobe@free.fr>wrote:

> Le 16/08/2013 18:55, Ajo Fod a écrit :
> > The algorithm computes the Hessian using an update rule. My question was
> > what if you can compute the hessian analytically?
> >
> > Hessian: http://en.wikipedia.org/wiki/Hessian_matrix
> > Gradient: http://en.wikipedia.org/wiki/Gradient
>
> We do have support to help generating second derivatives in the analysis
> package, but don't use them yet in the optimizers.
>
> best regards,
> Luc
>
> >
> > Cheers,
> > -Ajo
> >
> >
> > On Fri, Aug 16, 2013 at 9:39 AM, Luc Maisonobe <Luc.Maisonobe@free.fr
> >wrote:
> >
> >> Le 15/08/2013 22:59, Ajo Fod a écrit :
> >>> Hello,
> >>>
> >>> Is'nt there an advantage to being able to compute the Jacobian of the
> >>> gradient precisely at a point?
> >>>
> >>> If so, is there a class that uses the Jacobian instead of estimating
> the
> >>> jacobian from the last few iteration as
> >> NonLinearConjugateGradientOptimizer
> >>> does?
> >>
> >> I'm not sure what you really mean, but you can always pass an
> >> ObjectiveFunctionGradient holding any MultivariateVectorFunction to be
> >> used by the algorithm.
> >>
> >> Luc
> >>
> >>>
> >>> Thanks,
> >>> -Ajo
> >>>
> >>
> >>
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> >
>
>
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