Abstract
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 language | English |
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Pages (from-to) | 449-454 |
Number of pages | 6 |
Journal | IFAC Proceedings Volumes |
Volume | 37 |
Issue number | 9 |
DOIs | |
Publication status | Published - Jul 2004 |
Externally published | Yes |
Event | 7th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS 2004 - Cambridge, United States Duration: 5 Jul 2004 → 7 Jul 2004 |
Funding
Supported by the American Association of University Women, the University of Texas at Austin, and the National Science Foundation.
Keywords
- Dynamic optimization
- Implicit manifolds
- Implicit optimization
- Parametric optimization
- Real-time optimization