Two New Dai–Liao-Type Conjugate Gradient Methods for Unconstrained Optimization Problems
作者: Yutao ZhengBing Zheng
作者单位: 1Lanzhou University
刊名: Journal of Optimization Theory and Applications, 2017, Vol.175 (2), pp.502-509
来源数据库: Springer Journal
DOI: 10.1007/s10957-017-1140-1
关键词: Unconstrained optimizationDai–Liao-type methodsStrong convergenceGlobal convergenceStrong Wolfe line search65K0590C2690C30
英文摘要: In this paper, we present two new Dai–Liao-type conjugate gradient methods for unconstrained optimization problems. Their convergence under the strong Wolfe line search conditions is analysed for uniformly convex objective functions and general objective functions, respectively. Numerical experiments show that our methods can outperform some existing Dai–Liao-type methods by using Dolan and Moré’s performance profile.
原始语种摘要: In this paper, we present two new Dai–Liao-type conjugate gradient methods for unconstrained optimization problems. Their convergence under the strong Wolfe line search conditions is analysed for uniformly convex objective functions and general objective functions, respectively. Numerical experiments show that our methods can outperform some existing Dai–Liao-type methods by using Dolan and Moré’s performance profile.
全文获取路径: Springer  (合作)
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关键词翻译
关键词翻译
  • convergence 汇合
  • objective 接物镜
  • optimization 最佳化
  • uniformly 一律地
  • unconstrained 无约束
  • outperform 优越
  • conjugate 共轭的
  • existing 现行
  • gradient 倾斜度
  • search 搜索