Constrained Optimization Using Iterated Partial Kuhn-Tucker Vectors.

A frequently occurring problem is that of minimizing a convex function subject to a finite set of inequality constraints. Often what makes this problem difficult is the sheer number of constraints. That is, we could solve this problem for a smaller set of constraints, but solving for the total set causes difficulty. Here the authors discuss an approach which uses our ability to solve these partial problems to lead to a total solution. They illustrate the method with several examples in the last section of the paper. This approach will be somewhat heuristic in nature to promote understanding.