An efficient single-parameter scaling memoryless BFGS algorithm for solving large scale unconstrained optimization problems
发布时间：2020-05-18
点击次数：
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