Identify functions to be included in surrogate merit function


  • Alias: None

  • Arguments: None

  • Default: original_primary original_constraints

Child Keywords:


Description of Group

Dakota Keyword

Dakota Keyword Description

Required (Choose One)

Objective Formulation


Construct approximations of all primary functions


Construct approximation a single objective functions only


Augmented Lagrangian approximate subproblem formulation


Lagrangian approximate subproblem formulation

Required (Choose One)

Constraint Formulation


Use the constraints directly


Use linearized approximations to the constraints


Don’t use constraints


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).