Solving NLP problems with dynamic system approach based on smoothed penalty function

Abstract

In this work, a dynamical system approach for solving nonlinear programming (NLP) problem based on a smoothed penalty function is investigated. The proposed approach shows that an equilibrium point öf the dynamic system is stable and converge to optimal solutions of the corresponding nonlinear programming problem. Furthermore, relationships between optimal solutions for smooth and nonsmooth penalty problem are discussed. Finally, two practical examples are illustrated the applicability of the proposed dynamic system approach with Euler scheme