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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Select type of penalty or merit function
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 |
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].