Hybrid conjugate gradient methods for unconstrained optimization

Authors:
Jiangtao Mo;Nengzhu Gu;Zengxin Wei
Affiliations:
College of Science, Xian Jiaotong University, Xian, PR China,College of Mathematics and Information Science, Guangxi University, Nanning, Guangxi, PR China;College of Mathematics and Information Science, Guangxi University, Nanning, Guangxi, PR China;College of Mathematics and Information Science, Guangxi University, Nanning, Guangxi, PR China
Venue:
Optimization Methods & Software
Year:
2007

Citing 4
Cited 0

Efficient hybrid conjugate gradient techniques

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Optimization: algorithms and consistent approximations

Optimization: algorithms and consistent approximations
Testing Unconstrained Optimization Software

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A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property

SIAM Journal on Optimization

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Abstract

In this paper we propose two kinds of conjugate gradient methods for unconstrained optimization, based on the combinations of the presented conjugate gradient methods. The methods can be regarded as the modifications of the efficient hybrid methods proposed by Touati-Ahmed-Storey and Dai-Yuan. Under mild conditions, globally convergent results are proved. Primary numerical results of the new methods are encouraging.