Global convergence of proximal iteratively reweighted algorithm

被引：0
|
作者：
Tao Sun
Hao Jiang
Lizhi Cheng
机构：
[1] National University of Defense Technology,College of Science
[2] National University of Defense Technology,College of Computer
[3] National University of Defense Technology,The State Key Laboratory for High Performance Computation
来源：
Journal of Global Optimization | 2017年 / 68卷
关键词：
Proximal iteratively reweighted algorithm; Kurdyka–Łojasiewicz function; Convergence analysis; Parallel splitting; Alternating updating; 90C30; 90C26; 47N10;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we investigate the convergence of the proximal iteratively reweighted algorithm for a class of nonconvex and nonsmooth problems. Such problems actually include numerous models in the area of signal processing and machine learning research. Two extensions of the algorithm are also studied. We provide a unified scheme for these three algorithms. With the Kurdyka–Łojasiewicz property, we prove that the unified algorithm globally converges to a critical point of the objective function.
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页码：815 / 826
页数：11
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