A nonmonotone trust region method for unconstrained optimization
type: Item
instance of: scholarly article
title: A nonmonotone trust region method for unconstrained optimization
zbMATH Open document ID: 1094.65059
author: Jiangtao Mo
author: Kecun Zhang
author: Zengxin Wei
published in: Applied Mathematics and Computation
publication date: 2006-02-07T00:00:00Z
review text: The authors propose a combination of a nonmonotone technique, fixed step length and the trust region method, in order to minimize a twice continuously differentiable unconstrained function, in the aim to improve the algorithm of \textit{J. Nocedal} and \textit{Y. Yuan} [Appl. Optim. 14, 153--175 (1998; Zbl 0909.90243)] and make it more effective in practical implementation. The main difference between the proposed method and the original method is that in the former one a step length is computed by a line search when the trial step is not successful, whereas in the present method a step length is defined by a formula. It is proved under mild conditions that the algorithm is global and superlinear convergent. Some numerical test on known test problems with the original and the new proposed modification are provided.
reviewed by: Vincentiu Dumitru
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 90C51
zbMATH DE Number: 5004362
zbMATH Keywords: nonmonotone method
zbMATH Keywords: fixed step length
zbMATH Keywords: trust region method
zbMATH Keywords: unconstrained optimization
zbMATH Keywords: line search
zbMATH Keywords: global superlinear convergence
zbMATH Keywords: numerical examples
describes a project that uses: minpack
MaRDI profile type: Publication
full work available at URL: j.amc.2005.01.048
OpenAlex ID: W2071786165
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DOI: 10.1016/J.AMC.2005.01.048
description: scientific article; zbMATH DE number 5004362