Keren was interested in Monte Carlo optimization, so I thought I might
submit the one I wrote. It is structured as an optimizer coupled with
a set of Movers which propose moves. The optimizer holds a collection
of Movers and a local optimizer (which can be used to mix local
optimization with monte-carlo). Current movers take a set of particles
and a set of attributes and perturb all of those attributes on each of
the particles either uniformly in a a ball or with a normal
distribution.
Sounds good so far.
A class called MoveGuard keeps track of the information necessary to
revert if the monte carlo step is rejected. Note that as it stands,
writing a Mover in Python would take noticeably more work as the macro
does a good bit of work.
I would comment on this, but you seem to have forgotten to include the
patch. ;)
As a note, my current implementation uses the boost random number
library.
Anybody have any problem with that? If not, I will add the boost
requirement to the build system. I don't think it's unreasonable.
Ben
--
http://salilab.org/~ben/
"It is a capital mistake to theorize before one has data."
- Sir Arthur Conan Doyle