Dakota Reference Manual: approx_subproblem

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approx_subproblem

Identify functions to be included in surrogate merit function

## Specification

Alias: none

Argument(s): none

Default: original_primary original_constraints

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Required (Choose One) | Objective Formulation (Group 1) | original_primary | Construct approximations of all primary functions
single_objective | Construct approximation a single objective functions only
augmented_lagrangian_objective | Augmented Lagrangian approximate subproblem formulation
lagrangian_objective | Lagrangian approximate subproblem formulation
| Required (Choose One) | Constraint Formulation (Group 2) | original_constraints | Use the constraints directly
linearized_constraints | Use linearized approximations to the constraints
no_constraints | Don't use constraints

## Description

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. Second, the surrogate constraints in the approximate subproblem can be selected to be surrogates of the original constraints (`original_constraints`) or linearized approximations to the surrogate constraints (`linearized_constraints`), or constraints can be omitted from the subproblem (`no_constraints`).