Daniel, this classification is still confusing. In general, a sampler is a conformation generation scheme that follows a probability distribution: uniform (as in the example given by Daniel), Boltzmann (constant temperature MD or BD, as well as Monte Carlo with set_return_best(False)) or posterior probability (such as the Gibbs sampling in ISD).
An optimizer instead only aims at lowest energies (Conjugated Gradient, Steepest Descent... Monte Carlo with set_return_best(True))
On Fri, May 18, 2012 at 2:13 PM, Daniel Russel drussel@gmail.com wrote: > An optimizer attempts to improve the current configuration of the Model by > modifying optimized particle attributes so as to lower the score (there are > some exceptions such as Brownian Dynamics when in equilibrium, but those > are, I think, self-explanatory). The primary effect is to change particle > attributes. > > A Sampler in contrast tries to produce a number of good configurations of > the Model, often completely ignoring the Model's starting configuration (by > randomizing particles, for example). It returns ConfigurationSet that allows > you to load a configuration into the Model and then view it, save it or > score it. The final state of the particles after using a Sampler is > undefined. > > Each of Optimizer and Sampler can be given a ScoringFunction that will then > be used when evaluating and optimizing. By default it is > Model::create_scoring_function(), but one created with any other set of > restraints (a ScoringFunction will be created on the fly from a list of > restraints if you pass one instead). > > > On Fri, May 18, 2012 at 1:22 PM, Dina Schneidman duhovka@gmail.com wrote: >> >> Hi, >> >> I am trying to figure out the difference between sampler and optimizer. >> When each one should be used/developed? What is the relationship between >> them? >> How each one works with restraints and scoring functions? >> >> Dina >> _______________________________________________ >> IMP-dev mailing list >> IMP-dev@salilab.org >> https://salilab.org/mailman/listinfo/imp-dev > > > > _______________________________________________ > IMP-dev mailing list > IMP-dev@salilab.org > https://salilab.org/mailman/listinfo/imp-dev >