Nonlinear Programming

As outlined above, nonlinear programming (NLP) problems are intrinsically more difficult to solve than LP and QP problems. Because of the possibility of multiple feasible regions and multiple locally optimal points within such regions, there is no known way to determine with certainty that the problem is infeasible, the objective unbounded, or that an optimal solution is the "global optimum" across all feasible regions. However, many common NLP problems have a simpler structure than this general description, and are more amenable to solution.

It is important to realize that an NLP Solver, like the one in 1-2-3, applies the same method to all problems, even those that are really LPs or QPs. If you don't check the Assume Linear Model box in the Solver Options dialog, or (in the enhanced Solvers) select another solver from the dropdown list box in the Solver Parameters dialog, the default GRG Nonlinear Solver will be used. This solver may have difficulty with LP or QP problems that could have been solved easily with one of the other solvers.

Related Topics:

The GRG Method