I agree with Riccardo. If I understand correctly, a sampler is some sort
of ensemble generator isn't it? Is it equivalent to run the sampler once
and an optimizer N times (one like brownian dynamics, that would return
N different suboptimal states)?
Le 19/05/12 00:28, Riccardo Pellarin a écrit :
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<> 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<> 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
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