Convergence of Iterative Hard Thresholding Variants with Application to Asynchronous Parallel Methods for Sparse Recovery

被引：0
|
作者：
Haddock, Jamie [1 ]
Needell, Deanna [1 ]
Zaeemzadeh, Alireza [2 ]
Rahnavard, Nazanin [2 ]
机构：
[1] Univ Calif Los Angeles, Dept Math, Los Angeles, CA 90024 USA
[2] Univ Cent Florida, Sch Elect Engn & Comp Sci, Orlando, FL 32816 USA
来源：
CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS | 2019年
基金：
美国国家科学基金会;
关键词：
D O I：
10.1109/ieeeconf44664.2019.9048787
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Recently several asynchronous parallel algorithms for sparse recovery have been proposed. These methods share an estimation of the support of the signal between nodes, which then use this information in addition to their local estimation of the support to update via an iterative hard thresholding (IHT) method. We analyze a generalized version of the IHT method run on each of the nodes and show that this method performs at least as well as the standard IHT method. We perform numerical simulations that illustrate the potential advantage these methods enjoy over the standard IHT.
引用
收藏
页码：276 / 279
页数：4
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