Dakota Reference Manual  Version 6.15
Explore and Predict with Confidence
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scalarization


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

Specification

Alias: none

Argument(s): none

Child Keywords:

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
Optional scalarization_response_mapping

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 primary_response_mapping and secondary_response_mapping.

See Also

These keywords may also be of interest: