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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>
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