Numerical experience with a reduced Hessian method for large scale constrained optimization

Lorenz T. Biegler, Jorge Nocedal, Claudia Schmid, David Ternet

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

Abstract

The reduced Hessian SQP algorithm presented in Biegler et al. is developed in this paper into a practical method for large-scale optimization. The novelty of the algorithm lies in the incorporation of a correction vector that approximates the cross term ZT WY pY. This improves the stability and robustness of the algorithm without increasing its computational cost. The paper studies how to implement the algorithm efficiently, and presents a set of tests illustrating its numerical performance. An analytic example, showing the benefits of the correction term, is also presented.

Original languageEnglish (US)
Pages (from-to)45-67
Number of pages23
JournalComputational Optimization and Applications
Volume15
Issue number1
DOIs
StatePublished - Jan 1 2000

ASJC Scopus subject areas

  • Control and Optimization
  • Computational Mathematics
  • Applied Mathematics

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