Blurb:: 
(Experimental) How to select new points

Description::
\c adaptive_sampling is an experimental capability that is not ready
for production use at this time.

With batch or multi-point selection, the true model can be evaluated
in parallel and thus increase throughput before refitting our
surrogate model. This proposes a new challenge as the problem of
choosing a single point and choosing multiple points off a surrogate
are fundamentally different. Selecting the \c n best scoring
candidates is more than likely to generate a set of points clustered
in one area which will not be conducive to adapting the surrogate.

We have implemented several strategies for batch selection of points. 
These are described in the User's manual and are the subject of 
active research. 

The \c batch_selection strategies include: 
<ol>
<li> \c naive: 
<li> \c distance_penalty
<li> \c constant_liar
<li> \c topology
</ol>

Topics::
Examples::
Theory::
Faq::
See_Also::	
