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Three New Hybrid Conjugate Gradient Methods for Optimization

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DOI: 10.4236/am.2011.23035    5,418 Downloads   11,279 Views   Citations

ABSTRACT

In this paper, three new hybrid nonlinear conjugate gradient methods are presented, which produce suf?cient descent search direction at every iteration. This property is independent of any line search or the convexity of the objective function used. Under suitable conditions, we prove that the proposed methods converge globally for general nonconvex functions. The numerical results show that all these three new hybrid methods are efficient for the given test problems.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

A. Zhou, Z. Zhu, H. Fan and Q. Qing, "Three New Hybrid Conjugate Gradient Methods for Optimization," Applied Mathematics, Vol. 2 No. 3, 2011, pp. 303-308. doi: 10.4236/am.2011.23035.

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