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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Fit MLMC sample allocation to a mixture of terms of means and standard deviations.
Alias: none
Argument(s): none
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
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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. |
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
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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.
These keywords may also be of interest: