On the sufficient descent condition of the Hager-Zhang conjugate gradient methods
type: Item
instance of: scholarly article
title: On the sufficient descent condition of the Hager-Zhang conjugate gradient methods
zbMATH Open document ID: 1307.65084
author: Saman Babaie-Kafaki
published in: 4OR
publication date: 2014-12-17T00:00:00Z
review text: Conjugate gradient (CG) methods comprise a class of unconstrained optimization algorithms characterized by low memory requirements and strong global convergence properties. Although CG methods are not the fastest or most robust optimization algorithms available today, they remain very popular for engineers and mathematicians engaged in solving large-scale problems in the following form: \(\underset{x \in \mathbb R^n}\min f (x)\), where \(f : \mathbb R^n \to \mathbb R\) is a smooth nonlinear function and its gradient is available. Based on an eigenvalue study, the author establishes the sufficient descent condition of an extended class of the Hager-Zhang nonlinear conjugate gradient methods.
reviewed by: Hans Benker
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 90C06
zbMATH DE Number: 6381324
zbMATH Keywords: unconstrained optimization
zbMATH Keywords: conjugate gradient algorithm
zbMATH Keywords: eigenvalue
zbMATH Keywords: sufficient descent condition
zbMATH Keywords: global convergence
zbMATH Keywords: large-scale problem
zbMATH Keywords: Hager-Zhang nonlinear conjugate gradient method
describes a project that uses: L-BFGS
describes a project that uses: CG_DESCENT
MaRDI profile type: Publication
full work available at URL: s10288-014-0255-6
OpenAlex ID: W2015288657
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DOI: 10.1007/S10288-014-0255-6
description: scientific article; zbMATH DE number 6381324