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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Generate a D-optimal sampling design
Alias: none
Argument(s): none
Default: off
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
---|---|---|---|---|
Optional (Choose One) | Design Strategy (Group 1) | candidate_designs | Number of candidate sampling designs from which to select the most D-optimal | |
leja_oversample_ratio | Oversampling ratio for generating candidate point set |
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 aleatory_uncertain_variables. 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.
method sampling sample_type random samples = 20 d_optimal