On the convergence of \(s\)-dependent GFR conjugate gradient method for unconstrained optimization
type: Item
instance of: scholarly article
title: On the convergence of \(s\)-dependent GFR conjugate gradient method for unconstrained optimization
zbMATH Open document ID: 1398.65148
author: Yajing Gu
author: Wenling Zhao
author: Changyu Wang
published in: Numerical Algorithms
publication date: 2018-07-16T00:00:00Z
review text: One of the effective methods for solving the optimization problem is conjugate gradient method. For line search, the Fletcher-Reeves (FR) conjugate gradient method was considered by \textit{Y. H. Dai} and \textit{Y. Yuan} [IMA J. Numer. Anal. 16, No. 2, 155--164 (1996; Zbl 0851.65049)]. They obtained a generalized FR method which is called GFR method. Based on the generalized FR method, the authors present an \(s\)-dependent GFR method for unconstrained optimization problem. They also obtain two different kinds of estimations of upper bounds of a parameter which fulfils the FR-formula. Using several step-size rules, the global convergence of \(s\)-dependent GFR conjugate method is proved.
Mathematics Subject Classification ID: 65K10
zbMATH DE Number: 6904320
zbMATH Keywords: step-length
zbMATH Keywords: linear search
zbMATH Keywords: global convergence
zbMATH Keywords: conjugate gradient
reviewed by: Nada Djuranović-Miličić
describes a project that uses: minpack
MaRDI profile type: Publication
full work available at URL: s11075-017-0397-7
OpenAlex ID: W2748382761
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DOI: 10.1007/S11075-017-0397-7
description: scientific article; zbMATH DE number 6904320