1Blurb:: 2Lagrangian approximate subproblem formulation 3Description:: 4For SBL problems with nonlinear constraints, a number of algorithm 5formulations exist as described in \cite Eld06b 6and as summarized in the Advanced Examples section of 7the Models chapter of the Users Manual \cite UsersMan. 8First, the "primary" functions (that is, the objective 9functions or calibration terms) in the approximate subproblem can be 10selected to be surrogates of the original primary functions (\c 11original_primary), a single objective function (\c single_objective) 12formed from the primary function surrogates, or either an augmented 13Lagrangian merit function (\c augmented_lagrangian_objective) or a 14Lagrangian merit function (\c lagrangian_objective) formed from the 15primary and secondary function surrogates. The former option may 16imply the use of a nonlinear least squares method, a multiobjective 17optimization method, or a single objective optimization method to 18solve the approximate subproblem, depending on the definition of the 19primary functions. The latter three options all imply the use of a 20single objective optimization method regardless of primary function 21definition. 22 23Topics:: 24Examples:: 25Theory:: 26Faq:: 27See_Also:: 28