Optimal mechanism design using interior-point methods

This paper presents a primal–dual interior point algorithm for linearly constrained convex nonlinear programming and computational experience in solving optimal mechanism design problems using the algorithm. These problems are frequently formulated as convex programming problems, i.e. problems with linear constraints and an objective function formed as a sum of squared quantities. The algorithm has been implemented and tested on an IBM PC computer. The computational results demonstrated that the algorithm finds an approximate optimal solution in fewer iterations and function evaluations, the obtained solution usually being an interior feasible solution, and so the resulting method is very efficient and effective.