.. _method-multilevel_sampling-allocation_target-scalarization: """"""""""""" scalarization """"""""""""" Fit MLMC sample allocation to a mixture of terms of means and standard deviations. .. toctree:: :hidden: :maxdepth: 1 method-multilevel_sampling-allocation_target-scalarization-scalarization_response_mapping method-multilevel_sampling-allocation_target-scalarization-optimization **Specification** - *Alias:* None - *Arguments:* None **Child Keywords:** +-------------------------+--------------------+------------------------------------+-----------------------------------------------+ | Required/Optional | Description of | Dakota Keyword | Dakota Keyword Description | | | Group | | | +=========================+====================+====================================+===============================================+ | 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. | +----------------------------------------------+------------------------------------+-----------------------------------------------+ .. __: method-multilevel_sampling-allocation_target-scalarization-scalarization_response_mapping.html __ method-multilevel_sampling-allocation_target-scalarization-optimization.html **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 .. code-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 :dakkw:`model-nested-sub_method_pointer-primary_response_mapping` and :dakkw:`model-nested-sub_method_pointer-secondary_response_mapping`.