A New Descent Nonlinear Conjugate Gradient Method for Unconstrained Optimization

Hao Fan, Zhibin Zhu, Anwa Zhou

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DOI: 10.4236/am.2011.29154                

Abstract

In this paper, a new nonlinear conjugate gradient method is proposed for large-scale unconstrained optimization. The sufficient descent property holds without any line searches. We use some steplength technique which ensures the Zoutendijk condition to be held, this method is proved to be globally convergent. Finally, we improve it, and do further analysis.

Keywords

Large Scale Unconstrained Optimization, Conjugate Gradient Method, Sufficient Descent Property, Globally Convergent

Conflicts of Interest

The authors declare no conflicts of interest.

References

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