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Analysis on an Improved Global Convergence for a Spectral Conjugate Gradient Method

Songhai Deng, Xiaohong Chen, Zhong Wan


In this paper, we establish the theory of global convergence for a spectral conjugate gradient algorithm recently developed by Z. Wan etc. An assumption, that the inequalities are satisfied for any k, is first investigated by numerical experiments. It is shown that such assumption holds only for k large enough in solving some benchmark problems, not for all ones. Another contribution of this paper is to obtain the same convergence result under some weaker assumptions.


unconstrained optimization, conjugate gradient, global convergence

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