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.
|Number of pages||6|
|Journal||IFAC Proceedings Volumes|
|Publication status||Published - Jul 2004|
|Event||7th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS 2004 - Cambridge, United States|
Duration: 5 Jul 2004 → 7 Jul 2004
Bibliographical noteSupported 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