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 ( ``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 :cite:p:`Con00`.
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