A trust region method based on interior point techniques for nonlinear programming

Richard H. Byrd*, Jean Charles Gilbert, Jorge Nocedal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

895 Scopus citations

Abstract

An algorithm for minimizing a nonlinear function subject to nonlinear inequality constraints is described. It applies sequential quadratic programming techniques to a sequence of barrier problems, and uses trust regions to ensure the robustness of the iteration and to allow the direct use of second order derivatives. This framework permits primal and primal-dual steps, but the paper focuses on the primal version of the new algorithm. An analysis of the convergence properties of this method is presented.

Original languageEnglish (US)
Pages (from-to)149-185
Number of pages37
JournalMathematical Programming, Series B
Volume89
Issue number1
DOIs
StatePublished - Jan 1 2000

Keywords

  • Barrier method
  • Constrained optimization
  • Interior point method
  • Large-scale optimization
  • Nonlinear programming
  • Primal method
  • Primal-dual method
  • SQP iteration
  • Trust region method

ASJC Scopus subject areas

  • Software
  • Mathematics(all)

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