d_optimal
Generate a D-optimal sampling design
Specification
Alias: None
Arguments: None
Default: off
Child Keywords:
Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
---|---|---|---|
Optional (Choose One) |
Design Strategy |
Number of candidate sampling designs from which to select the most D-optimal |
|
Oversampling ratio for generating candidate point set |
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
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.
Examples
method
sampling
sample_type random
samples = 20
d_optimal