An interior algorithm for nonlinear optimization that combines line search and trust region steps

R. A. Waltz*, J. L. Morales, J. Nocedal, D. Orban

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

556 Scopus citations

Abstract

An interior-point method for nonlinear programming is presented. It enjoys the flexibility of switching between a line search method that computes steps by factoring the primal-dual equations and a trust region method that uses a conjugate gradient iteration. Steps computed by direct factorization are always tried first, but if they are deemed ineffective, a trust region iteration that guarantees progress toward stationarity is invoked. To demonstrate its effectiveness, the algorithm is implemented in the Knitro [6,28] software package and is extensively tested on a wide selection of test problems.

Original languageEnglish (US)
Pages (from-to)391-408
Number of pages18
JournalMathematical Programming
Volume107
Issue number3
DOIs
StatePublished - Jul 2006

ASJC Scopus subject areas

  • Software
  • Mathematics(all)

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