merit_function

Select type of penalty or merit function

Specification

• Alias: None

• Arguments: None

• Default: augmented_lagrangian_merit

Child Keywords:

Required/Optional	Description of Group	Dakota Keyword	Dakota Keyword Description
Required (Choose One)	Merit Function	penalty_merit	Use penalty merit function
		adaptive_penalty_merit	Use adaptive penalty merit function
		lagrangian_merit	Use first-order Lagrangian merit function
		augmented_lagrangian_merit	Use combined penalty and zeroth-order Lagrangian merit function

Description

Following optimization of the approximate subproblem, the candidate iterate is evaluated using a merit function, which can be selected to be a simple penalty function with penalty ramped by surrogate_based_local iteration number ( penalty_merit), an adaptive penalty function where the penalty ramping may be accelerated in order to avoid rejecting good iterates which decrease the constraint violation ( adaptive_penalty_merit), a Lagrangian merit function which employs first-order Lagrange multiplier updates ( lagrangian_merit), or an augmented Lagrangian merit function which employs both a penalty parameter and zeroth-order Lagrange multiplier updates ( augmented_lagrangian_merit). When an augmented Lagrangian is selected for either the subproblem objective or the merit function (or both), updating of penalties and multipliers follows the approach described in [CGT00].