Blurb::
Initial number of samples for sampling-based methods

Description::
The \c initial_samples keyword is used to define the number of initial
samples (i.e., randomly chosen sets of variable values) at which to
execute a model.  The initial samples may later be augmented in an
iterative process.

<b> Default Behavior </b>

By default, Dakota will use the minimum number of samples required by
the chosen method.

<b> Usage Tips </b>

To obtain linear sensitivities or to construct a linear response
surface, at least dim+1 samples should be used, where "dim" is the
number of variables.  For sensitivities to quadratic terms or
quadratic response surfaces, at least (dim+1)(dim+2)/2 samples are
needed.  For uncertainty quantification, we recommend at least 10*dim
samples.  For \c variance_based_decomp, we recommend hundreds to
thousands of samples.  Note that for \c variance_based_decomp, the
number of simulations performed will be N*(dim+2).


Topics::	

Examples::
\verbatim
method
  sampling
    sample_type random
    initial_samples = 20
    refinement_samples = 5
\endverbatim

Theory::
Faq::
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