[Octave Forge] Possible addition of gradient-descent framework for optim package

[Daniel Kraft]

Hi!

(I sent this already to Olaf some time ago, but I think it makes sense
to give everyone a possible say in the discussion.)

I currently include functions for a gradient-descent method for shape
optimisation in my level-set package.  (You can look at them in the
development sources, they are not yet in a release -- look at the so_*
functions.)

They implement a generic descent method including potentially parallel
line search.  Problems can be specified generically by defining suitable
callbacks.  The full descent process can also be instrumented via
callbacks, so that you can produce plots at each step, save plots for
selected iterations as PDF, or do whatever you want.  I use that for my
own research.

Another highlight is the ability to save the full descent run to a log
file and replay it later.  This way, you can, for instance, run a
calculation over night and later investigate each iteration
interactively, use it to produce plots or do other stuff with it
(without the need to recalculate).  You can look at the demos for the
functions if you wish to see it in action.

Currently, these functions depend on level-set and do only shape
optimisation.  The points of dependence are, however, only two of three
single lines of code.  So I think that it would be fairly easy to
introduce another callback that breaks the coupling, so that the methods
can be used for all kinds of optimisation problems.

Do you think that they could be included into the optim package instead
of level-set if this is done?  I can, of course, provide the full patch.
 Carne also offered to include them into image when I talked to him at
OctConf, but we both agree that they do, of course, fit much better into
optim.

Yours,
Daniel

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