Absolute minimizer in convex programming by exponential penalty.

Abstract

We consider a nonlinear convex program. Under some general hy-potheses, we prove that approximate solutions obtained by exponential penalty converge toward a particular solution of the original convex program as the penalty parameter goes to zero. This particular solu-tion is called the absolute minimizer and is characterized as the unique solution of a hierarchical scheme of minimax problems.