Multi-Parametric Nonlinear Programming and the Evaluation of Implicit Optimization Model Adequacy

Elaine T. HALE, S. Joe QIN

Research output: Journal PublicationsJournal Article (refereed)peer-review

3 Citations (Scopus)


An algorithmic framework for numerically approximating multiparametric nonlinear programming (mp-NLP) solutions is given, along with a method that uses mp-NLP for evaluating the adequacy of the nominal model used in Implicit Optimization. The mp-NLP algorithm builds on numerical methods for single parameter nonlinear programming and for the approximation of implicit manifolds. An example problem is presented.
Original languageEnglish
Pages (from-to)449-454
Number of pages6
JournalIFAC Proceedings Volumes
Issue number9
Publication statusPublished - Jul 2004
Externally publishedYes
Event7th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS 2004 - Cambridge, United States
Duration: 5 Jul 20047 Jul 2004

Bibliographical note

Supported by the American Association of University Women, the University of Texas at Austin, and the National Science Foundation.


  • Dynamic optimization
  • Implicit manifolds
  • Implicit optimization
  • Parametric optimization
  • Real-time optimization


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