Two new Dai-Liao-type conjugate gradient methods for unconstrained optimization problems
type: Item
instance of: scholarly article
title: Two new Dai-Liao-type conjugate gradient methods for unconstrained optimization problems
zbMATH Open document ID: 1380.65108
author: Bing Zheng
author: Yu-Tao Zheng
published in: Journal of Optimization Theory and Applications
publication date: 2017-12-15T00:00:00Z
review text: This paper introduces two Dai-Liao-type methods by modifying the conjugate gradient parameter for unconstrained optimization problems. The convergence for uniformly convex objective functions is established. The efficiency of the proposed methods are illustrated by some numerical results.
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C26
Mathematics Subject Classification ID: 90C30
zbMATH DE Number: 6819195
zbMATH Keywords: unconstrained optimization
zbMATH Keywords: Dai-Liao-type methods
zbMATH Keywords: strong convergence
zbMATH Keywords: global convergence
zbMATH Keywords: strong Wolfe line search
zbMATH Keywords: numerical result
describes a project that uses: CUTEst
MaRDI profile type: Publication
full work available at URL: s10957-017-1140-1
OpenAlex ID: W2736319691
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DOI: 10.1007/S10957-017-1140-1
description: scientific article; zbMATH DE number 6819195