Globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds

Andy Conn; N. I. M. Gould; Philippe L. Toint

Globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds

Andy Conn, N. I. M. Gould, Philippe L. Toint

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Résumé

The global and local convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems are considered. In such methods, simple bound constraints are treated separately from more general constraints and the stopping rules for the inner minimization algorithm have this in mind. Global convergence is proved, and it is established that a potentially troublesome penalty parameter is bounded away from zero.

langue originale	Anglais
Pages (de - à)	545-572
Nombre de pages	28
journal	SIAM Journal on Numerical Analysis
Volume	28
Numéro de publication	2
Etat de la publication	Publié - 1 avr. 1991

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Cgt5Accepted author manuscript, 2,81 MB

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1 Terminé
1 Actif

ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire
Sartenaer, A. & TOINT, P.
1/01/87 → …
Projet: Axe de recherche
LANCELOT: LANCELOT, un logiciel pour l'optimisation non linéaire de grande taille
TOINT, P., Sartenaer, A., Gould, N. I. M. & Conn, A.
1/09/87 → 1/09/00
Projet: Recherche

Contient cette citation

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abstract = "The global and local convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems are considered. In such methods, simple bound constraints are treated separately from more general constraints and the stopping rules for the inner minimization algorithm have this in mind. Global convergence is proved, and it is established that a potentially troublesome penalty parameter is bounded away from zero.",

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N2 - The global and local convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems are considered. In such methods, simple bound constraints are treated separately from more general constraints and the stopping rules for the inner minimization algorithm have this in mind. Global convergence is proved, and it is established that a potentially troublesome penalty parameter is bounded away from zero.

AB - The global and local convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems are considered. In such methods, simple bound constraints are treated separately from more general constraints and the stopping rules for the inner minimization algorithm have this in mind. Global convergence is proved, and it is established that a potentially troublesome penalty parameter is bounded away from zero.

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M3 - Article

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JO - SIAM Journal on Numerical Analysis

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Globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds

Résumé

Accès au document

Autres fichiers et liens

Empreinte digitale

Projets

ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire

LANCELOT: LANCELOT, un logiciel pour l'optimisation non linéaire de grande taille

Contient cette citation