Dakota Reference Manual  Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
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merit_function


Select type of penalty or merit function

Specification

Alias: none

Argument(s): none

Default: augmented_lagrangian_merit

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[15].