Identify functions to be included in surrogate merit function

Specification

Alias: none

Argument(s): none

Default: original_primary original_constraints

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).