Norm descent conjugate gradient methods for solving symmetric nonlinear equations
type: Item
instance of: scholarly article
title: Norm descent conjugate gradient methods for solving symmetric nonlinear equations
zbMATH Open document ID: 1326.65063
author: Chunjie Wu
author: Soon-Yi Wu
author: Yunhai Xiao
published in: Journal of Global Optimization
publication date: 2015-09-22T00:00:00Z
review text: The authors propose a family of conjugate gradient methods for solving large-scale symmetric nonlinear equations. The proposed methods do not require the Jacobian information of the quations and do not store any matrix at each iteration. The global convergence of the method is established under certain technical conditions. Some experimental results are presented to show the effectiveness of the proposed methods.
Mathematics Subject Classification ID: 65H10
zbMATH DE Number: 6484165
zbMATH Keywords: unconstrained optimization
zbMATH Keywords: conjugate gradient method
zbMATH Keywords: backtracking line search
zbMATH Keywords: numerical examples
zbMATH Keywords: large-scale symmetric nonlinear equations
zbMATH Keywords: global convergence
describes a project that uses: CG_DESCENT
MaRDI profile type: Publication
full work available at URL: s10898-014-0218-7
OpenAlex ID: W1987583991
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DOI: 10.1007/S10898-014-0218-7
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description: scientific article; zbMATH DE number 6484165