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所属单位：

中南大学

发表刊物：

JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS

摘要：

In this paper, an improved spectral conjugate gradient algorithm is developed for solving nonconvex unconstrained optimization problems. Different from the existent methods, the spectral and conjugate parameters are chosen such that the obtained search direction is always sufficiently descent as well as being close to the quasi-Newton direction. With these suitable choices, the additional assumption in the method proposed by Andrei on the boundedness of the spectral parameter is removed. Under some mild conditions, global convergence is established. Numerical experiments are employed to demons

合写作者：

Xiaohong Chen

第一作者：

Songhai Deng

论文类型：

基础研究

通讯作者：

Zhong Wan

文献类型：

J

卷号：

157

期号：

3

页面范围：

820-842

是否译文：

否

发表时间：

2013-06-01