Dakota Reference Manual  Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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nested


A model whose responses are computed through the use of a sub-iterator

Specification

Alias: none

Argument(s): none

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
Optional optional_interface_pointer Pointer to interface that provides non-nested responses
Required sub_method_pointer The sub_method_pointer specifies the method block for the sub-iterator

Description

Instead of appealing directly to a primary interface, a nested model maps variables to responses by executing a secondary iterator, or a "sub-iterator". In other words, a function evaluation of the primary study consists of a solution of an entire secondary study - potentially many secondary function evaluations.

The sub-iterator in turn operates on a sub-model. The sub-iterator responses may be combined with non-nested contributions from an optional interface specification.

A sub_method_pointer must be provided in order to specify the method block describing the sub-iterator. The remainder of the model is specified under that keyword.

A optional_interface_pointer points to the interface specification and optional_interface_responses_pointer points to a responses specification describing the data to be returned by this interface). This interface is used to provide non-nested data, which is then combined with data from the nested iterator using the primary_response_mapping and secondary_response_mapping inputs (see mapping discussion below).

Examples

An example of variable and response mappings is provided below:

primary_variable_mapping  = '' '' 'X'   'Y'
secondary_variable_mapping = '' '' 'mean' 'mean'
primary_response_mapping  = 1. 0. 0. 0. 0. 0. 0. 0. 0.
secondary_response_mapping = 0. 0. 0. 1. 3. 0. 0. 0. 0.
               0. 0. 0. 0. 0. 0. 1. 3. 0.

The variable mappings correspond to 4 top-level variables, the first two of which employ the default mappings from active top-level variables to sub-model variables of the same type (option 3 above) and the latter two of which are inserted into the mean distribution parameters of sub-model variables 'X' and 'Y' (option 1 above). The response mappings define a 3 by 9 matrix corresponding to 9 inner loop response attributes and 3 outer loop response functions (one primary response function and 2 secondary functions, such as one objective and two constraints). Each row of the response mapping is a vector which is multiplied (i.e, with a dot-product) against the 9 sub-iterator values to determine the outer loop function. Consider a UQ example with 3 response functions, each providing a mean, a standard deviation, and one level mapping (if no level mappings are specified, the responses would only have a mean and standard deviation). The primary response mapping can be seen to extract the first value from the inner loop, which would correspond to the mean of the first response function. This mapped sub-iterator response becomes a single objective function, least squares term, or generic response function at the outer level, as dictated by the top-level response specification. The secondary response mapping maps the fourth sub-iterator response function plus 3 times the fifth sub-iterator response function (mean plus 3 standard deviations) into one top-level nonlinear constraint and the seventh sub-iterator response function plus 3 times the eighth sub-iterator response function (mean plus 3 standard deviations) into another top-level nonlinear constraint, where these top-level nonlinear constraints may be inequality or equality, as dictated by the top-level response specification. Note that a common case is for each sub-iterator response to be mapped to a unique outer loop response (for example, in the nested UQ case where one wants to determine an interval on each inner loop statistic). In these simple cases, the response mapping would define an identity matrix.

See Also

These keywords may also be of interest: