Projets par an
Résumé
A mechanism for proving global convergence in SQP-filter methods for nonlinear programming (NLP) is described. Such methods are characterized by their use of the dominance concept of multiobjective optimization, instead of a penalty parameter whose adjustment can be problematic. The main point of interest is to demonstrate how convergence for NLP can be induced without forcing sufficient descent in a penalty-type merit function. The proof relates to a prototypical algorithm, within which is allowed a range of specific algorithm choices associated with the Hessian matrix representation, updating the trust region radius, and feasibility restoration.
langue originale | Anglais |
---|---|
Pages (de - à) | 44-59 |
Nombre de pages | 16 |
journal | SIAM Journal on Optimization |
Volume | 13 |
Numéro de publication | 1 |
Les DOIs | |
Etat de la publication | Publié - 1 janv. 2003 |
Empreinte digitale
Examiner les sujets de recherche de « On the global convergence of a filter-SQP algorithm ». Ensemble, ils forment une empreinte digitale unique.-
ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire
1/01/87 → …
Projet: Axe de recherche
-