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

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: (1) the new search direction possesses not only a suf...

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Bibliographic Details
Published in:Mathematical problems in engineering 2018-01, Vol.2018 (2018), p.1-11
Main Authors: Pham, Hongtruong, Jin, Zhongzhou, Wang, Songhua, Li, Xiangrong
Format: Article
Language:English
Subjects:
Algorithms
Applied mathematics
Central processing units
CPUs
Distributed processing
Mathematical models
Mathematical problems
Multiprocessing
Optimization models
Queuing theory
Searching
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Summary: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: (1) the new search direction possesses not only a sufficient descent property but also a trust region feature; (2) the presented algorithm has global convergence for nonconvex functions; (3) the numerical experiment showed that the new algorithm is more effective than similar algorithms.
ISSN:1024-123X
1563-5147
DOI:10.1155/2018/4729318