数学物理学报 ›› 2021, Vol. 41 ›› Issue (3): 837-847.

• 论文 • 上一篇    下一篇

强Wolfe线搜索下的修正PRP和HS共轭梯度法

马国栋()   

  1. 广西民族大学数学与物理学院 南宁 530006
  • 收稿日期:2020-04-11 出版日期:2021-06-26 发布日期:2021-06-09
  • 作者简介:马国栋, E-mail: mgd2006@163.com
  • 基金资助:
    广西自然科学基金(2018GXNSFAA281099);国家自然科学基金(11771383);玉林师范学院科研基金(2019YJKY16)

Improved PRP and HS Conjugate Gradient Methods with the Strong Wolfe Line Search

Guodong Ma()   

  1. College of Mathematics and Physics, Guangxi University for Nationalities, Nanning 530006
  • Received:2020-04-11 Online:2021-06-26 Published:2021-06-09
  • Supported by:
    the NSF of Guangxi(2018GXNSFAA281099);the NSFC(11771383);the Research Project of Yulin Normal University(2019YJKY16)

摘要:

共轭梯度法是求解大规模无约束优化问题最有效的方法之一.结合强Wolfe线搜索的第二个不等式,该文提出了修正的PRP和HS公式.在常规假设下,由修正的PRP方法和HS方法所产生的搜索方向均满足充分下降条件,且得到了较大的参数σ取值范围,并证明了两个修正的方法是全局收敛的.最后,对新算法进行数值试验,并与其它同类算法进行比对,其结果验证了该文所提出算法的有效性.

关键词: 无约束优化, 共轭梯度法, 强Wolfe线搜索, 全局收敛性

Abstract:

The conjugate gradient method is one of the most effective methods for solving large-scale unconstrained optimization. Combining the second inequality of the strong Wolfe line search, two new conjugate parameters are constructed. Under usual assumptions, it is proved that the improved PRP and HS conjugate gradient methods satisfy sufficient descent condition with the greater range of parameter in the strong Wolfe line search and converge globally for unconstrained optimization. Finally, two group numerical experiments for the proposed methods and their comparisons are tested, the numerical results and their corresponding performance files are reported, which show that the proposed methods are promising.

Key words: Unconstrained optimization, Conjugate gradient method, Strong Wolfe line search, Global convergence

中图分类号: 

  • O221