A sequential quadratic programming algorithm with an additional equality constrained phase

José Luis Morales, Jorge Nocedal*, Yuchen Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

A sequential quadratic programming (SQP) method is presented that aims to overcome some of the drawbacks of contemporary SQP methods. It avoids the difficulties associated with indefinite quadratic programming subproblems by defining this subproblem to be always convex. The novel feature of the approach is the addition of an equality constrained quadratic programming (EQP) phase that promotes fast convergence and improves performance in the presence of ill conditioning. This EQP phase uses exact second-order information and can be implemented using either a direct solve or an iterative method. The paper studies the global and local convergence properties of the new algorithm and presents a set of numerical experiments to illustrate its practical performance.

Original languageEnglish (US)
Pages (from-to)553-579
Number of pages27
JournalIMA Journal of Numerical Analysis
Volume32
Issue number2
DOIs
StatePublished - Apr 2012

Keywords

  • Constrained optimization
  • Nonlinear programming
  • Sequential quadratic programming

ASJC Scopus subject areas

  • Mathematics(all)
  • Computational Mathematics
  • Applied Mathematics

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