A primal-dual interior-point method for nonlinear programming with strong global and local convergence properties

André L. Tits*, Andreas Wächter, Sasan Bakhtiari, Thomas J. Urban, Craig T. Lawrence

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

An exact-penalty-function-baaed scheme - inspired from an old idea due to Mayne and Polak [Math. Program., 11 (1976), pp. 67-80] - is proposed for extending to general smooth constrained optimization problems any given feasible interior-point method for inequality constrained problems. It is shown that the primal-dual interior-point framework allows for a simpler penalty parameter update rule than the one discussed and analyzed by the originators of the scheme in the context of first order methods of feasible direction. Strong global and local convergence results are proved under mild assumptions. In particular, (i) the proposed algorithm does not suffer a common pitfall recently pointed out by Wächter and Biegler [Math. Program., 88 (2000), pp. 565-574]; and (ii) the positive definiteness assumption on the Hessian estimate, made in the original version of the algorithm, is relaxed, allowing for the use of exact Hessian information, resulting in local quadratic convergence. Promising numerical results are reported.

Original languageEnglish (US)
Pages (from-to)173-199
Number of pages27
JournalSIAM Journal on Optimization
Volume14
Issue number1
DOIs
StatePublished - 2004

Keywords

  • Constrained optimization
  • Feasibility
  • Nonlinear programming
  • Primal-dual interior-point methods

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science

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