Enlarging the region of convergence of Newton's method for constrained optimization

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

In this paper, we consider Newton's method for solving the system of necessary optimality conditions of optimization problems with equality and inequality constraints. The principal drawbacks of the method are the need for a good starting point, the inability to distinguish between local maxima and local minima, and, when inequality constraints are present, the necessity to solve a quadratic programming problem at each iteration. We show that all these drawbacks can be overcome to a great extent without sacrificing the superlinear convergence rate by making use of exact differentiable penalty functions introduced by Di Pillo and Grippo (Ref. 1). We also show that there is a close relationship between the class of penalty functions of Di Pillo and Grippo and the class of Fletcher (Ref. 2), and that the region of convergence of a variation of Newton's method can be enlarged by making use of one of Fletcher's penalty functions.

Original languageEnglish (US)
Pages (from-to)221-252
Number of pages32
JournalJournal of Optimization Theory and Applications
Volume36
Issue number2
DOIs
StatePublished - Feb 1982
Externally publishedYes

Keywords

  • Constrained minimization
  • differentiable exact penalty functions
  • Newton's method
  • superlinear convergence

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'Enlarging the region of convergence of Newton's method for constrained optimization'. Together they form a unique fingerprint.

Cite this