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影响因子：

3.78

DOI码：

10.1109/ACCESS.2020.2992340

所属单位：

数学与统计学院

教研室：

信息与计算科学

发表刊物：

IEEE-access

刊物所在地：

USA

关键字：

Computational efficiency, Convergence of numerical methods, Optimization methods, Algorithm design and analysis

摘要：

In this paper, a new spectral scaling memoryless BFGS algorithm is developed for solving large scale unconstrained optimization problems, where the scaling parameter is chosen so as to minimize all the eigenvalues of search direction matrices. The search directions in this algorithm are also proved to satisfy the Dai-Liao conjugate condition. With this advantage of such search directions, a memoryless BFGS update formula is constructed and an algorithm is developed based on acceleration scheme and restart criterion. Under mild assumptions, global convergence of the algorithm is proved. Numerical experiments demonstrate that the developed algorithm is more robust and efficient than the existing similar ones.

合写作者：

Songhai Deng

第一作者：

Jing, Lv

论文类型：

期刊论文

论文编号：

10.1109/ACCESS.2020.2992340

文献类型：

J

卷号：

8

页面范围：

85664-85674

是否译文：

否

发表时间：

2020-04-30

收录刊物：

SCI