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.

*Default Behavior*

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

*Usage Tips*

D-optimal designs are only supported for
:ref:`variables:uncertain:auv`.  The default candidate-based
D-optimal strategy works for all submethods except incremental LHS (
``lhs`` with ``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::

.. code-block::

    method
      sampling
        sample_type random
        samples = 20
        d_optimal


Theory::

Faq::

See_Also::
