A class of nonmonotone conjugate gradient methods for nonconvex functions
type: Item
instance of: scholarly article
title: A class of nonmonotone conjugate gradient methods for nonconvex functions
zbMATH Open document ID: 1006.65066
author: Yun Liu
author: Zengxin Wei
published in: Applied Mathematics. Series B (English Edition)
publication date: 2003-03-04T00:00:00Z
review text: The authors consider the global convergence of a class of nonmonotone conjugate gradient methods for unconstrained optimization problems \(\min_{x\in\mathbb{R}^n} f(x)\), where \(f(x)\) is a nonconvex function. The paper extends the results for convex functions to nonconvex functions. No numerical tests are given.
reviewed by: Hans Benker
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 90C52
zbMATH DE Number: 1802903
zbMATH Keywords: global convergence
zbMATH Keywords: nonmonotone conjugate gradient methods
zbMATH Keywords: unconstrained optimization
MaRDI profile type: Publication
full work available at URL: s11766-002-0047-1
OpenAlex ID: W1993741432
cites work: Q5563083
cites work: Q3313210
cites work: Global Convergence Properties of Conjugate Gradient Methods for Optimization
cites work: Q4312995
cites work: An Assessment of Nonmonotone Linesearch Techniques for Unconstrained Optimization
cites work: A Nonmonotone Line Search Technique for Newton’s Method
cites work: Global convergece of the bfgs algorithm with nonmonotone linesearch∗∗this work is supported by national natural science foundation$ef:
cites work: A class of nonmonotone conjugate gradient methods for unconstrained optimization
cites work: Q4238924
DOI: 10.1007/S11766-002-0047-1
description: scientific article; zbMATH DE number 1802903