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 |
Use penalty merit function |
|
Use adaptive penalty merit function |
|||
Use first-order Lagrangian merit function |
|||
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].