Nonconvex Proximal Incremental Aggregated Gradient Method with Linear Convergence

被引：0
作者：
Wei Peng
Hui Zhang
Xiaoya Zhang
机构：
[1] National University of Defense Technology,Department of Mathematics
来源：
Journal of Optimization Theory and Applications | 2019年 / 183卷
关键词：
Error bound; Linear convergence; Nonconvex; Incremental aggregated gradient; 90C26; 90C06; 90C15;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we study the proximal incremental aggregated gradient algorithm for minimizing the sum of L-smooth nonconvex component functions and a proper closed convex function. By exploiting the L-smooth property and using an error bound condition, we can show that the method still enjoys some desired linear convergence properties, even for nonconvex minimization. Actually, we show that the generated iterative sequence globally converges to the stationary point set. Moreover, we give an explicit computable stepsize threshold to guarantee that both the objective value and iterative sequences are R-linearly convergent.
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页码：230 / 245
页数：15
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