Blurb:: Generate a D-optimal sampling design

Description::

This option will generate a sampling design that is approximately
determinant-optimal (D-optimal) by downselecting from a set of
candidate sample points.

<b> Default Behavior </b>

If not specified, a standard sampling design (MC or LHS) will be
generated.  When \c d_optimal is specified, 100 candidate designs will
be generated and the most D-optimal will be selected.

<b> Usage Tips </b>

D-optimal designs are only supported for \ref
topic-aleatory_uncertain_variables.  The default candidate-based
D-optimal strategy works for all submethods except incremental LHS (\c
lhs with \c refinement_samples).  The Leja sampling option only works
for continuous variables, and when used with LHS designs, the
candidates point set will be Latin, but the final design will not be.

Topics::	
Examples::
\verbatim
method
  sampling
    sample_type random
    samples = 20
    d_optimal
\endverbatim
Theory::
Faq::
See_Also::	
