augmented_lagrangian_objective

Augmented Lagrangian approximate subproblem formulation

Specification

  • Alias: None

  • Arguments: None

Description

For SBL problems with nonlinear constraints, a number of algorithm formulations exist as described in [ED06] and as summarized in Surrogate-Based Local Minimization. First, the “primary” functions (that is, the objective functions or calibration terms) in the approximate subproblem can be selected to be surrogates of the original primary functions ( original_primary), a single objective function ( ,single_objective) formed from the primary function surrogates, or either an augmented Lagrangian merit function ( augmented_lagrangian_objective) or a Lagrangian merit function ( lagrangian_objective) formed from the primary and secondary function surrogates. The former option may imply the use of a nonlinear least squares method, a multiobjective optimization method, or a single objective optimization method to solve the approximate subproblem, depending on the definition of the primary functions. The latter three options all imply the use of a single objective optimization method regardless of primary function definition.