Daniel Russel wrote: > 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