Blurb::
Select type of penalty or 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 (\c 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 (\c adaptive_penalty_merit), a Lagrangian merit function
which employs first-order Lagrange multiplier updates (\c
lagrangian_merit), or an augmented Lagrangian merit function which
employs both a penalty parameter and zeroth-order Lagrange multiplier
updates (\c 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 \cite Con00.



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