Blurb::
Construct approximation a single objective functions only
Description::
For SBL problems with nonlinear constraints, a number of algorithm
formulations exist as described in :cite:p:`Eld06b`
and as summarized in :ref:`adv_meth:sbm:sblm`.
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.
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