1Blurb:: Generate a D-optimal sampling design
2
3Description::
4
5This option will generate a sampling design that is approximately
6determinant-optimal (D-optimal) by downselecting from a set of
7candidate sample points.
8
9<b> Default Behavior </b>
10
11If not specified, a standard sampling design (MC or LHS) will be
12generated.  When \c d_optimal is specified, 100 candidate designs will
13be generated and the most D-optimal will be selected.
14
15<b> Usage Tips </b>
16
17D-optimal designs are only supported for \ref
18topic-aleatory_uncertain_variables.  The default candidate-based
19D-optimal strategy works for all submethods except incremental LHS (\c
20lhs with \c refinement_samples).  The Leja sampling option only works
21for continuous variables, and when used with LHS designs, the
22candidates point set will be Latin, but the final design will not be.
23
24Topics::
25Examples::
26\verbatim
27method
28  sampling
29    sample_type random
30    samples = 20
31    d_optimal
32\endverbatim
33Theory::
34Faq::
35See_Also::
36