Dakota Reference Manual: merit_function

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merit_function

Select type of penalty or merit function

## Specification

Alias: none

Argument(s): none

Default: augmented_lagrangian_merit

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Required (Choose One) | Merit Function (Group 1) | 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[14].