Flexible penalty functions for nonlinear constrained optimization

Frank E. Curtis, Jorge Nocedal

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


We propose a globalization strategy for nonlinear constrained optimization. The method employs a 'flexible' penalty function to promote convergence, where during each iteration the penalty parameter can be chosen as any number within a prescribed interval, rather than a fixed value. This increased flexibility in the step acceptance procedure is designed to promote long productive steps for fast convergence. An analysis of the global convergence properties of the approach in the context of a line search sequential quadratic programming method and numerical results for the KNITRO software package are presented.

Original languageEnglish (US)
Pages (from-to)749-769
Number of pages21
JournalIMA Journal of Numerical Analysis
Issue number4
StatePublished - Oct 2008


  • Constrained optimization
  • Global convergence
  • Nonlinear programming
  • Penalty functions
  • Sequential quadratic programming

ASJC Scopus subject areas

  • Mathematics(all)
  • Computational Mathematics
  • Applied Mathematics


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