[IMP-dev] Proposal: new OptimizerState class

[Ben Webb <>]

We currently have a State class, which you can use to update some kind 
of shared state every time the score is calculated.

You could also in principle use State to generate a trajectory during an 
optimization (e.g. see test/md_optimizer/test_md_optimizer.py). This 
works fine for optimizers such as molecular dynamics, where each step of 
the optimization has a single evaluation of the score function. But it's 
no good for optimizers like CG and SD, which may do multiple trial 
evaluations per step - you only want to include the accepted points in 
the trajectory. You could also imagine doing other things during an 
optimization - for example, periodic recentering of the system, 
resetting of thermostats, or rescaling of particle velocities.

Thus, I propose an OptimizerState class, which is similar to State but 
is added to an optimizer rather than a model. Its update method would 
then be called at every successful optimization step, rather than at 
every score evaluation. To prevent people adding OptimizerStates to the 
model, the current State should be renamed ScoreState and both 
OptimizerState and ScoreState should derive from a common State base 
class. A derived MDOptimizerState or similar class could be used in turn 
as a base class for MD-specific stuff such as velocity scaling.

	Ben
--
                      http://salilab.org/~ben/
"It is a capital mistake to theorize before one has data."
	- Sir Arthur Conan Doyle