penalty_merit
Use penalty merit function
Specification
Alias: None
Arguments: None
Description
Second, the surrogate constraints in the approximate subproblem can be
selected to be surrogates of the original constraints (
original_constraints) or linearized approximations to the surrogate
constraints ( linearized_constraints), or constraints can be omitted
from the subproblem ( no_constraints). 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 SBL 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].
