Dakota Reference Manual: scalarization

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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:

- variance