Asymptotic analysis for penalty and barrier methods in convex and linear programming

A. Auslender, R. Cominetti, M. Haddou

Research output: Contribution to journalArticlepeer-review

91 Scopus citations

Abstract

We consider a wide class of penalty and barrier methods for convex programming which includes a number of specific functions proposed in the literature. We provide a systematic way to generate penalty and barrier functions in this class, and we analyze the existence of primal and dual optimal paths generated by these penalty methods, as well as their convergence to the primal and dual optimal sets. For linear programming we prove that these optimal paths converge to single points.

Original languageEnglish
Pages (from-to)43-62
Number of pages20
JournalMathematics of Operations Research
Volume22
Issue number1
DOIs
StatePublished - Feb 1997

Keywords

  • Asymptotic analysis
  • Convex and linear programming
  • Optimal trajectory
  • Penalty methods

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