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

Access to Document

10.1007/BF01399316

Cite this

@article{e06dcdbf8c0e4a47a6af7efdc66f5a6c,

title = "Partitioned variable metric updates for large structured optimization problems",

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. {\textcopyright} 1982 Springer-Verlag.",

author = "A. Griewank and Ph.L. Toint",

year = "1982",

month = feb,

day = "1",

doi = "10.1007/BF01399316",

language = "English",

volume = "39",

pages = "119--137",

journal = "Numerische Mathematik",

publisher = "Springer New York",

}

TY - JOUR

T1 - Partitioned variable metric updates for large structured optimization problems

AU - Griewank, A.

AU - Toint, Ph.L.

PY - 1982/2/1

Y1 - 1982/2/1

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=0013358480&partnerID=8YFLogxK

U2 - 10.1007/BF01399316

DO - 10.1007/BF01399316

M3 - Article

VL - 39

SP - 119

EP - 137

JO - Numerische Mathematik

JF - Numerische Mathematik

ER -

Partitioned variable metric updates for large structured optimization problems

Abstract

Access to Document

Other files and links

Fingerprint

ADALGOPT: ADALGOPT - Advanced algorithms in nonlinear optimization

Cite this

Partitioned variable metric updates for large structured optimization problems

Abstract

Access to Document

Other files and links

Fingerprint

Projects

ADALGOPT: ADALGOPT - Advanced algorithms in nonlinear optimization

Cite this