Dakota Reference Manual: d_optimal

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d_optimal

Generate a D-optimal sampling design

## Specification

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

## 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