A Feasible BFGS Interior Point Algorithm for Solving Strongly Convex Minimization Problems

Abstract : We propose a BFGS primal-dual interior point method for minimizing a convex function on a convex set defined by equality and inequality constraints. The algorithm generates feasible iterates and consists in computing approximate solutions of the optimality conditions perturbed by a sequence of positive parameters $\mu$ converging to zero. We prove that it converges q-superlinearly for each fixed $\mu$ and that it is globally convergent when $\mu\to0$.
Type de document :
Rapport
[Research Report] RR-3500, INRIA. 1998
Liste complète des métadonnées

https://hal.inria.fr/inria-00073185
Contributeur : Rapport de Recherche Inria <>
Soumis le : mercredi 24 mai 2006 - 12:06:02
Dernière modification le : vendredi 25 mai 2018 - 12:02:03
Document(s) archivé(s) le : dimanche 4 avril 2010 - 21:43:51

Fichiers

Identifiants

  • HAL Id : inria-00073185, version 1

Citation

Paul Armand, Jean Charles Gilbert, Sophie Jan-Jégou. A Feasible BFGS Interior Point Algorithm for Solving Strongly Convex Minimization Problems. [Research Report] RR-3500, INRIA. 1998. 〈inria-00073185〉

Partager

Métriques

Consultations de la notice

266

Téléchargements de fichiers

302