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Y. H. Dai and Y. Yuan, “An Efficient Hybrid Conjugate Gradient Method for Unconstrained Optimization,” Annals of Operations Research, Vol. 103, No. 1-4, 2001, pp. 33-47. doi:10.1023/A:1012930416777

has been cited by the following article:

• TITLE: Three New Hybrid Conjugate Gradient Methods for Optimization

  AUTHORS: Anwa Zhou, Zhibin Zhu, Hao Fan, Qian Qing

  KEYWORDS: Conjugate Gradient Method, Descent Direction, Global Convergence

  JOURNAL NAME: Applied Mathematics, Vol.2 No.3, March 24, 2011

  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.