commons-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Konstantin Berlin <kber...@gmail.com>
Subject Re: [math] Using the hessian in scalar unconstrained optimization
Date Sun, 18 Aug 2013 19:56:48 GMT
The general problem is not computationally tractable. You can trying
stochastic algorithms, like simulated annealing or genetic programmig, but
results depend on the problem. There is no point in computing derivatives
in that case either.

On Sunday, August 18, 2013, Ajo Fod wrote:

> Looks like Joptimizer is restricted to solving convex problems.
>
> My application is to minimize a generic non-linear function with linear
> constraints. Know of anything that does it?
>
> -Ajo
>
>
> On Thu, Aug 15, 2013 at 5:48 PM, Konstantin Berlin <kberlin@gmail.com<javascript:;>
> >wrote:
>
> > There would be an advantage, true. I don't know if commons has one
> > (doesn't look like it). You can also try http://www.joptimizer.com/
> >
> > On Thu, Aug 15, 2013 at 4:59 PM, Ajo Fod <ajo.fod@gmail.com<javascript:;>>
> wrote:
> > > 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?
> > >
> > > Thanks,
> > > -Ajo
> >
> > ---------------------------------------------------------------------
> > To unsubscribe, e-mail: dev-unsubscribe@commons.apache.org<javascript:;>
> > For additional commands, e-mail: dev-help@commons.apache.org<javascript:;>
> >
> >
>

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message