Duality and exact penalization via a generalized augmented Lagrangian function

X.X. Huang, Xiaoqi Yang

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic research

Abstract

In this paper, we introduce generalized augmented Lagrangian by relaxing the convexity assumption on the usual augmenting function. Applications are given to establish strong duality and exact penalty representation for the problem of minizing an extended real valued function. More specifically, a strong duality result based on the generalized augmented Lagrangian is established, and a necessary and sufficient condition for the exact penalty representation in the framework of generalized augmented Lagrangian is obtained.
Original languageEnglish
Title of host publicationOptimization and control with applications
PublisherSpringer Science+Business Media
Pages101-114
Number of pages14
ISBN (Electronic)9780387242552
ISBN (Print)9780387242545
DOIs
Publication statusPublished - 2005

Keywords

  • Extended real-valued function
  • Generalized augmented Lagrangian
  • Duality
  • Exact penalty representation

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