Interior-point methods for nonconvex nonlinear programming: Filter methods and merit functions

Hande Y. Benson, Robert J. Vanderbei, David F. Shanno

Research output: Contribution to journalArticle

73 Scopus citations

Abstract

Recently, Fletcher and Leyffer proposed using filter methods instead of a merit function to control steplengths in a sequential quadratic programming algorithm. In this paper, we analyze possible ways to implement a filter-based approach in an interior-point algorithm. Extensive numerical testing shows that such an approach is more efficient than using a merit function alone.

Original languageEnglish (US)
Pages (from-to)257-272
Number of pages16
JournalComputational Optimization and Applications
Volume23
Issue number2
DOIs
StatePublished - Nov 1 2002

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics

Keywords

  • Filter methods
  • Interior-point methods
  • Nonconvex optimization
  • Nonlinear programming

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