scalarization

Fit MLMC sample allocation to a mixture of terms of means and standard deviations.

Specification

  • Alias: None

  • Arguments: None

Child Keywords:

Required/Optional

Description of Group

Dakota Keyword

Dakota Keyword Description

Optional

scalarization_response_mapping

Coefficients for linear scalarization (combination) of responses

Optional

optimization

Solve the optimization problem for the sample allocation by numerical optimization in the case of sampling estimator targeting the scalarization.

Description

Fit MLMC sample allocation to control the variance of the estimator for a mixture of terms of means and standard deviations. The exact scalarized formulation is given by the keyword scalarization_response_mapping.

Examples

The following method block

method,
 model_pointer = 'HIERARCH'
        multilevel_sampling
   pilot_samples = 20 seed = 1237
   convergence_tolerance = .01
   allocation_target = scalarization
    scalarization_response_mapping = 1 0 0 0
                                                 0 0 1 3

uses the standard_deviation as sample allocation target by computing its variance. In this example, we assume a problem with two responses where the first line in scalarization_response_mapping refers to the first response, the second line to the second response. In the first line we only use 1 times the mean as quantity of interest. For the second response, we use 1 time the mean plus 3 times the standard devitation of the second quantity of interested. This behavior mimics the keywords model-nested-sub_method_pointer-primary_response_mapping and model-nested-sub_method_pointer-secondary_response_mapping.