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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Use first-order Lagrangian merit function
Alias: none
Argument(s): none
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[14].