Nonlinear augmented lagrangian for nonconvex multiobjective optimization

Chunrong Chen, Edwin Tai Chiu Cheng, Shengjie Li, Xiaoqi Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

In this paper, based on the ordering relations induced by a pointed, closed and convex cone with a nonempty interior, we propose a nonlinear augmented Lagrangian dual scheme for a nonconvex multiobjective optimization problem by applying a class of vector-valued nonlinear augmented Lagrangian penalty functions. We establish the weak and strong duality results, necessary and sufficient conditions for uniformly exact penalization and exact penalization in the framework of nonlinear augmented Lagrangian. Our results include several ones in the literature as special cases.
Original languageEnglish
Pages (from-to)157-174
Number of pages18
JournalJournal of Industrial and Management Optimization
Volume7
Issue number1
DOIs
Publication statusPublished - 1 Feb 2011

Keywords

  • Exact penalization
  • Multiobjective optimization
  • Nonlinear augmented lagrangian
  • Ordering cone
  • Set-Valued maps
  • Strong duality

ASJC Scopus subject areas

  • Business and International Management
  • Strategy and Management
  • Control and Optimization
  • Applied Mathematics

Cite this