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

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

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