Nonlinear conjugate gradient methods with sufficient descent condition for large-scale unconstrained optimization
type: Item
instance of: scholarly article
title: Nonlinear conjugate gradient methods with sufficient descent condition for large-scale unconstrained optimization
zbMATH Open document ID: 1184.65066
DOI: 10.1155/2009/243290
published in: Mathematical Problems in Engineering
publication date: 2009-11-13T00:00:00Z
full work available at URL: https://eudml.org/doc/45812
review text: Summary: Two nonlinear conjugate gradient-type methods for solving unconstrained optimization problems are proposed. An attractive property of the methods, is that, without any line search, the generated directions always descend. Under some mild conditions, global convergence results for both methods are established. Preliminary numerical results show that these proposed methods are promising, and competitive with the well-known PRP method.
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 90C06
zbMATH DE Number: 5632454
zbMATH Keywords: large-scale
zbMATH Keywords: comparison of methods
zbMATH Keywords: nonlinear conjugate gradient-type methods
zbMATH Keywords: unconstrained optimization
zbMATH Keywords: global convergence
zbMATH Keywords: numerical results
zbMATH Keywords: PRP method
Wikidata QID: Q58648924
author: Jianguo Zhang
author: Yunhai Xiao
author: Zengxin Wei
describes a project that uses: CUTEr
describes a project that uses: SCALCG
describes a project that uses: CUTE
MaRDI profile type: Publication
OpenAlex ID: W2076396137
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description: scientific article; zbMATH DE number 5632454