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