Augmented Lagrangian functions for constrained optimization problems

Y. Y. Zhou, Xiaoqi Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

In this paper, in order to obtain some existence results about solutions of the augmented Lagrangian problem for a constrained problem in which the objective function and constraint functions are noncoercive, we construct a new augmented Lagrangian function by using an auxiliary function. We establish a zero duality gap result and a sufficient condition of an exact penalization representation for the constrained problem without the coercive or level-bounded assumption on the objective function and constraint functions. By assuming that the sequence of multipliers is bounded, we obtain the existence of a global minimum and an asymptotically minimizing sequence for the constrained optimization problem.
Original languageEnglish
Pages (from-to)95-108
Number of pages14
JournalJournal of Global Optimization
Volume52
Issue number1
DOIs
Publication statusPublished - 1 Jan 2012

Keywords

  • Asymptotically minimizing sequence
  • Augmented Lagrangian function
  • Coercive
  • Constrained optimization problem

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Optimization
  • Applied Mathematics
  • Management Science and Operations Research

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