A primal-dual interior-point relaxation method for nonlinear programs

TL;DR

This paper introduces a novel primal-dual interior-point relaxation method for nonlinear programming that does not require iterates to be interior points, ensuring global convergence without regularity assumptions.

Contribution

It presents a new line-search primal-dual interior-point method with an adaptive barrier penalty, capable of handling ill-posed and infeasible problems without regularity conditions.

Findings

Method converges to approximate KKT points or stationary points.

Effective on well-posed, ill-posed, and infeasible problems.

Preliminary numerical results demonstrate efficiency and broad applicability.

Abstract

We prove that the classic logarithmic barrier problem is equivalent to a particular logarithmic barrier positive relaxation problem with barrier and scaling parameters. Based on the equivalence, a line-search primal-dual interior-point relaxation method for nonlinear programs is presented. Our method does not require any primal or dual iterates to be interior-points, which is prominently different from the existing interior-point methods in the literature. A new logarithmic barrier penalty function dependent on both primal and dual variables is used to prompt the global convergence of the method, where the penalty parameter is updated adaptively. Without assuming any regularity condition, it is proved that our method will terminate at an approximate KKT point of the original problem provided the barrier parameter tends zero. Otherwise, either an approximate infeasible stationary point or an approximate singular stationary point of the original problem will be found. Some preliminary numerical results are reported, including the results for a well-posed problem for which many line-search interior-point methods were demonstrated not to be globally convergent, a feasible problem for which the LICQ and the MFCQ fail to hold at the solution and an infeasible problem, and for some standard test problems of the CUTE collection. These results show that our algorithm is not only efficient for well-posed feasible problems, but also is applicable for some ill-posed feasible problems and some even infeasible problems.