On Sat, Dec 15, 2007 at 08:41:16AM +0100, Mario Rodriguez wrote:
>
> > Rescaling and using a sum of squared errors approach allowed lbfgs to
> > converge, so thanks for the suggestions. I'd still like to hear the
> > list's suggestions for additional methods to add to the optimization
> > toolkit in Maxima. I have had one off-list suggestion which I will try
> > when I have a little more time (next week).
> >
>
> What about Simulated Annealing or Genetic Algorithms?
>
> They are stochastic global optimization algorithms. And the function to
> be optimized doesn't need to be differentiable; in fact, they are also
> used in discrete optimization.
>
> Simulated Annealing should be easier to program.
I think simulated annealing would be a good choice for a general
purpose highly robust strictly numerical optimization algorithm. It is
also possible to specify inequality constraints using SA by rejecting
proposals that do not meet the constraints. However, it requires the
user to generate proposals which may be difficult for the user to
understand.
--
Daniel Lakeland
dlakelan at street-artists.org
http://www.street-artists.org/~dlakelan