Wolfe线搜索下一类记忆梯度算法的全局收敛性（英文）

Global Convergence of a Class of Memory Gradient Method with the Wolfe Line Search

在本文中,首先我们提出一个记忆梯度算法,并讨论其在Wolfe线搜索下的下降性和全局收敛性.进一步地,我们将此算法推广到更一般的情形.最后,我们对这类记忆梯度方法的数值表现进行测试,并与PRP, FR, HS, LS, DY和CD共轭梯度法进行比较,数值结果表明这类算法是有效的.

In this paper, we first propose a memory gradient algorithm, and discuss its descent property and global convergence under the Wolfe line search. Further, we extend the algorithm to a more general form. Finally we test the numerical performance and compare the numerical results of this class of algorithms with the PRP, FR, HS, LS, DY and CD conjugate gradient methods, which indicate the class of algorithms proposed in this paper are effective.