original_primary
Construct approximations of all primary functions
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.
