Solving unconstrained optimization problems with some three-term conjugate gradient methods

被引:1
作者
Arman, Ladan [1 ]
Xu, Yuanming [1 ]
Bayat, Mohammad Reza [2 ]
Long, Liping [3 ]
机构
[1] Beihang Univ, Sch Aeronaut Sci & Engn, Beijing, Peoples R China
[2] Beihang Univ, Sch Elect & Informat Engn, Beijing, Peoples R China
[3] Chinese Acad Sci, Inst Mechaincs, Beijing 100190, Peoples R China
来源
TAMKANG JOURNAL OF MATHEMATICS | 2023年 / 54卷 / 02期
关键词
Conjugate gradient method; unconstrained optimization; global convergence; strong Wolfe line search; SUFFICIENT DESCENT PROPERTY; CONVERGENCE CONDITIONS; GLOBAL CONVERGENCE; ALGORITHM; DIRECTION; FAMILY;
D O I
10.5556/j.tkjm.54.2023.4185
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In this paper, based on the efficient Conjugate Descent (CD) method, two generalized CD algorithms are proposed to solve the unconstrained optimization problems. These methods are three-term conjugate gradient methods which the generated directions by using the conjugate gradient parameters and independent of the line search satisfy in the sufficient descent condition. Furthermore, under the strong Wolfe line search, the global convergence of the proposed methods are proved. Also, the preliminary numerical results on the CUTEst collection are presented to show effectiveness of our methods.
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页码:139 / 154
页数:16
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