method-multilevel_sampling-allocation_target-scalarization

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

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 \c scalarization_response_mapping. 

Topics::

Examples::
The following method block 
\verbatim
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
\endverbatim

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 \ref model-nested-sub_method_pointer-primary_response_mapping and \ref model-nested-sub_method_pointer-secondary_response_mapping.

Theory::


Faq::
See_Also:: method-multilevel_sampling-allocation_target-variance	