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一种混合的HS-DY共轭梯度法

戴志锋,陈兰平   

  1. 首都师范大学数学系,首都师范大学数学系 北京 100037 ,北京 100037
  • 出版日期:2005-04-14 发布日期:2005-04-14

戴志锋,陈兰平. 一种混合的HS-DY共轭梯度法[J]. 计算数学, 2005, 27(4): 429-436.

A MIXED HS-DY CONJUGATE GRADIENT METHODS

  1. Dai Zhifeng Chen Lanping (Department of Mathematics, Capital Normal University, Beijing 100037, China)
  • Online:2005-04-14 Published:2005-04-14

本文在HS方法和DY方法的基础上,综合两者的优势,提出了一种求解无约束优化问题的新的混合共轭梯度法。在Wolfe线搜索下,不需给定下降条件,证明了算法的全局收敛性。数值试验表明,新算法较之HS方法和PR方法更加有效。

In this paper, we propose a mixed conjugate gradient method for unconstrained optimization based on Hestenes-stiefel Algorithms and Dai-Yuan Algorithms, which has taken the advantages of two Algorithms. We prove it can ensure the convergence under the Wolfe line search and without the descent condition. Numerical experiments show that the algorithm is efficient by comparing with HS conjugate gradient methods and PR conjugate gradient methods.

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