Nonsmooth method for constrained optimization

TL;DR

This paper introduces a new implicit iterative algorithm for solving inequality constrained optimization problems using an exact penalty approach, demonstrating rapid convergence and applicability to large-scale problems.

Contribution

The paper presents a novel fixed point method for a regularized penalty functional in constrained optimization, enhancing convergence speed and scalability.

Findings

Demonstrates rapid convergence of the proposed method

Shows effectiveness on large-scale inequality constrained problems

Validates applicability and feasibility through experiments

Abstract

We propose an implicit iterative algorithm for an exact penalty method arising from inequality constrained optimization problems. A rapidly convergent fixed point method is developed for a regularized penalty functional. The applicability and feasibility of the proposed method is demonstrated using large scale inequality constrained problems.