Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

A primal-dual augmented Lagrangian method for non-linear optimization

Abstract : We present a primal-dual augmented Lagrangian algorithm for NLP. The algorithm is based on the Newton method applied to a sequence of per- turbed KKT systems which comes by introducing both an augmented La- grangian and a log-barrier penalty. The globalization is done by means of a control of the iterates in the primal-dual space all along the iterations. Global and asymptotic convergence results are shown. Numerical tests are also presented. We show that the method is robust in the sense that it is able to solve degenerate problems for which the Jacobian of constraints is rank deficient.
Type de document :
Communication dans un congrès
Liste complète des métadonnées
Contributeur : Paul Armand Connectez-vous pour contacter le contributeur
Soumis le : mercredi 18 décembre 2013 - 09:37:09
Dernière modification le : mercredi 22 décembre 2021 - 11:58:03


  • HAL Id : hal-00920243, version 1



Paul Armand, Joël Benoist, Riadh Omheni. A primal-dual augmented Lagrangian method for non-linear optimization. EUROPT 2013, Jun 2013, Florence, Italy. ⟨hal-00920243⟩



Consultations de la notice