Partitioned variable metric updates for large structured optimization problems

A. Griewank; Ph.L. Toint

doi:10.1007/BF01399316

Partitioned variable metric updates for large structured optimization problems

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Résumé

This paper presents a minimization method based on the idea of partitioned updating of the Hessian matrix in the case where the objective function can be decomposed in a sum of convex "element" functions. This situation occurs in a large class of practical problems including nonlinear finite elements calculations. Some theoretical and algorithmic properties of the update are discussed and encouraging numerical results are presented. © 1982 Springer-Verlag.

langue originale	Anglais
Pages (de - à)	119-137
Nombre de pages	19
journal	Numerische Mathematik
Volume	39
Les DOIs	https://doi.org/10.1007/BF01399316
Etat de la publication	Publié - 1 févr. 1982

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10.1007/BF01399316

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ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire
Sartenaer, A. & TOINT, P.
1/01/87 → …
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AB - This paper presents a minimization method based on the idea of partitioned updating of the Hessian matrix in the case where the objective function can be decomposed in a sum of convex "element" functions. This situation occurs in a large class of practical problems including nonlinear finite elements calculations. Some theoretical and algorithmic properties of the update are discussed and encouraging numerical results are presented. © 1982 Springer-Verlag.

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U2 - 10.1007/BF01399316

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M3 - Article

SN - 0029-599X

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JO - Numerische Mathematik

JF - Numerische Mathematik

ER -

Partitioned variable metric updates for large structured optimization problems

Résumé

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Projets

ADALGOPT: ADALGOPT - Algorithmes avancés en optimisation non-linéaire

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