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Mathematical Problems in Engineering
Volume 2018, Article ID 4729318, 11 pages
https://doi.org/10.1155/2018/4729318
Research Article

A Conjugate Gradient Algorithm under Yuan-Wei-Lu Line Search Technique for Large-Scale Minimization Optimization Models

1College of Mathematics and Information Science, Guangxi University, Nanning, Guangxi 530004, China
2School of Mathematics and Statistics, Baise University, Baise, Guangxi 533000, China
3Business School, Guangxi University, Nanning, Guangxi 530004, China
4Thai Nguyen University of Economics and Business Administration, Thai Nguyen, Vietnam

Correspondence should be addressed to Xiangrong Li; moc.361@86ilrx

Received 17 October 2017; Accepted 27 November 2017; Published 15 January 2018

Academic Editor: Guillermo Cabrera-Guerrero

Copyright © 2018 Xiangrong Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper gives a modified Hestenes and Stiefel (HS) conjugate gradient algorithm under the Yuan-Wei-Lu inexact line search technique for large-scale unconstrained optimization problems, where the proposed algorithm has the following properties: the new search direction possesses not only a sufficient descent property but also a trust region feature; the presented algorithm has global convergence for nonconvex functions; the numerical experiment showed that the new algorithm is more effective than similar algorithms.