Broyden–Fletcher–Goldfarb–Shanno算法
数学
共轭梯度法
稳健性(进化)
算法
梯度下降
非线性共轭梯度法
行搜索
有界函数
水准点(测量)
趋同(经济学)
拟牛顿法
数学优化
牛顿法
计算机科学
非线性系统
人工智能
经济
半径
化学
地理
基因
异步通信
数学分析
物理
量子力学
生物化学
人工神经网络
经济增长
计算机安全
计算机网络
大地测量学
作者
Poom Kumam,Auwal Bala Abubakar,Maulana Malik,Abdulkarim Hassan Ibrahim,Nuttapol Pakkaranang,Bancha Panyanak
标识
DOI:10.1016/j.cam.2023.115304
摘要
This article presents a new hybrid conjugate gradient (CG) algorithm for solving unconstrained optimization problem. The search direction is defined as a combination of Hestenes–Stiefel (HS) and the Liu–Storey (LS) CG parameters and is close to the direction of the memoryless Broyden–Fletcher–Goldferb–Shanno (BFGS) quasi-Newton direction. In addition, the search direction is descent and bounded. The global convergence of the algorithm is obtained under the Wolfe-type and Armijo-type line searches. Numerical experiments on some benchmark test problems is carried out to depict the efficiency and robustness of the hybrid algorithm. Furthermore, a practical application of the algorithm in motion control of robot manipulator and image restoration is provided.
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