Blurb::
Construct approximations of all primary functions
Description::
For SBL problems with nonlinear constraints, a number of algorithm
formulations exist as described in \cite Eld06b 
and as summarized in the Advanced Examples section of
the Models chapter of the Users Manual \cite UsersMan.
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.

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