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

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	119-137
Number of pages	19
Journal	Numerische Mathematik
Volume	39
DOIs	https://doi.org/10.1007/BF01399316
Publication status	Published - 1 Feb 1982

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ADALGOPT: ADALGOPT - Advanced algorithms in nonlinear optimization
SARTENAER, A. & TOINT, P.
1/01/87 → …
Project: Research Axis

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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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JF - Numerische Mathematik

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Partitioned variable metric updates for large structured optimization problems

Abstract

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