Blurb::
Identify functions to be included in surrogate merit function
Description::
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 (\c original_primary), a
single objective function (\c single_objective) formed from the
primary function surrogates, or either an augmented Lagrangian merit
function (\c augmented_lagrangian_objective) or a Lagrangian merit
function (\c 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. Second, the surrogate constraints in the approximate
subproblem can be selected to be surrogates of the original
constraints (\c original_constraints) or linearized approximations to
the surrogate constraints (\c linearized_constraints), or constraints
can be omitted from the subproblem (\c no_constraints).


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