A primal-dual trust-region algorithm for non-convex nonlinear programming

Andy Conn; Nick Gould; Dominique Orban; Philippe Toint

A primal-dual trust-region algorithm for non-convex nonlinear programming

Andy Conn, Nick Gould, Dominique Orban, Philippe Toint

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Résumé

A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trust-region model to ensure descent on a suitable merit function. Convergence is proved to second-order critical points from arbitrary starting points. Numerical results are presented for general quadratic programs.

langue originale	Anglais
Pages (de - à)	215-249
Nombre de pages	35
journal	Mathematical Programming
Volume	87
Numéro de publication	2
Etat de la publication	Publié - 1 avr. 2000

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1 Terminé
1 Actif

ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire
Sartenaer, A. (Co-investigateur) & Toint, P. (Co-investigateur)
1/01/87 → …
Projet: Axe de recherche
Algorithmes de points intérieurs
Toint, P. (Responsable du Projet) & Sartenaer, A. (Chercheur)
1/01/97 → 28/02/02
Projet: Recherche

Contient cette citation

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title = "A primal-dual trust-region algorithm for non-convex nonlinear programming",

abstract = "A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trust-region model to ensure descent on a suitable merit function. Convergence is proved to second-order critical points from arbitrary starting points. Numerical results are presented for general quadratic programs.",

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AU - Orban, Dominique

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N2 - A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trust-region model to ensure descent on a suitable merit function. Convergence is proved to second-order critical points from arbitrary starting points. Numerical results are presented for general quadratic programs.

AB - A new primal-dual algorithm is proposed for the minimization of non-convex objective functions subject to general inequality and linear equality constraints. The method uses a primal-dual trust-region model to ensure descent on a suitable merit function. Convergence is proved to second-order critical points from arbitrary starting points. Numerical results are presented for general quadratic programs.

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M3 - Article

AN - SCOPUS:0000317378

SN - 0025-5610

VL - 87

SP - 215

EP - 249

JO - Mathematical Programming

JF - Mathematical Programming

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A primal-dual trust-region algorithm for non-convex nonlinear programming

Résumé

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Autres fichiers et liens

Empreinte digitale

Projets

ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire

Algorithmes de points intérieurs

Contient cette citation