Dakota Reference Manual  Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
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original_primary


Construct approximations of all primary functions

Specification

Alias: none

Argument(s): none

Description

For SBL problems with nonlinear constraints, a number of algorithm formulations exist as described in[21] and as summarized in the Advanced Examples section of the Models chapter of the Users Manual[5]. First, the "primary" functions (that is, the objective functions or calibration terms) in the approximate subproblem can be selected to be surrogates of the original primary functions (original_primary), a single objective function (single_objective) formed from the primary function surrogates, or either an augmented Lagrangian merit function (augmented_lagrangian_objective) or a Lagrangian merit function (lagrangian_objective) formed from the primary and secondary function surrogates. The former option may imply the use of a nonlinear least squares method, a multiobjective optimization method, or a single objective optimization method to solve the approximate subproblem, depending on the definition of the primary functions. The latter three options all imply the use of a single objective optimization method regardless of primary function definition.