Convergence analysis of a class of nonlinear penalization methods for constrained optimization via first-order necessary optimality conditions

X. X. Huang, Xiaoqi Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

We propose a scheme to solve constrained optimization problems by combining a nonlinear penalty method and a descent method. A sequence of nonlinear penalty optimization problems is solved to generate a sequence of stationary points, i.e., each point satisfies a first-order necessary optimality condition of a nonlinear penalty problem. Under some conditions, we show that any limit point of the sequence satisfies the first-order necessary condition of the original constrained optimization problem.
Original languageEnglish
Pages (from-to)311-332
Number of pages22
JournalJournal of Optimization Theory and Applications
Volume116
Issue number2
DOIs
Publication statusPublished - 1 Feb 2003

Keywords

  • differentiability
  • Lipschitz functions
  • locally
  • necessary optimality conditions
  • Nonlinear penalization
  • smooth approximate variational principle

ASJC Scopus subject areas

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

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