scholarly journals 2D Normalized Iterative Hard Thresholding Algorithm for Fast Compressive Radar Imaging

2017 ◽  
Vol 9 (6) ◽  
pp. 619 ◽  
Author(s):  
Gongxin Li ◽  
Jia Yang ◽  
Wenguang Yang ◽  
Yuechao Wang ◽  
Wenxue Wang ◽  
...  
Keyword(s):  
Radar Imaging ◽  
Hard Thresholding ◽  
Iterative Hard Thresholding ◽  
Thresholding Algorithm

A Note on the Complexity of Proximal Iterative Hard Thresholding Algorithm

2015 ◽  
Vol 3 (4) ◽  
pp. 459-473 ◽  
Author(s):  
Xue Zhang ◽  
Xiao-Qun Zhang
Keyword(s):  
Hard Thresholding ◽  
Iterative Hard Thresholding ◽  
Thresholding Algorithm

scholarly journals HDIHT: A High-Accuracy Distributed Iterative Hard Thresholding Algorithm for Compressed Sensing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 49180-49186 ◽  
Author(s):  
Xiaming Chen ◽  
Zhuang Qi ◽  
Jianlong Xu
Keyword(s):  
Compressed Sensing ◽  
High Accuracy ◽  
Hard Thresholding ◽  
Iterative Hard Thresholding ◽  
Thresholding Algorithm

scholarly journals Block normalised iterative hard thresholding algorithm for compressed sensing

2019 ◽  
Vol 55 (17) ◽  
pp. 957-959 ◽  
Author(s):  
Xiaobo Zhang ◽  
Wenbo Xu ◽  
Jiaru Lin ◽  
Yifei Dang
Keyword(s):  
Compressed Sensing ◽  
Hard Thresholding ◽  
Iterative Hard Thresholding ◽  
Thresholding Algorithm

Convergence of iterative hard-thresholding algorithm with continuation

2016 ◽  
Vol 11 (4) ◽  
pp. 801-815 ◽  
Author(s):  
Tao Sun ◽  
Lizhi Cheng
Keyword(s):  
Hard Thresholding ◽  
Iterative Hard Thresholding ◽  
Thresholding Algorithm

scholarly journals Jointly low-rank and bisparse recovery: Questions and partial answers

2019 ◽  
Vol 18 (01) ◽  
pp. 25-48 ◽  
Author(s):  
Simon Foucart ◽  
Rémi Gribonval ◽  
Laurent Jacques ◽  
Holger Rauhut
Keyword(s):  
Restricted Isometry Property ◽  
Low Rank ◽  
Hard Thresholding ◽  
Linear Measurements ◽  
Iterative Hard Thresholding ◽  
Rank One ◽  
Thresholding Algorithm

We investigate the problem of recovering jointly [Formula: see text]-rank and [Formula: see text]-bisparse matrices from as few linear measurements as possible, considering arbitrary measurements as well as rank-one measurements. In both cases, we show that [Formula: see text] measurements make the recovery possible in theory, meaning via a nonpractical algorithm. In case of arbitrary measurements, we investigate the possibility of achieving practical recovery via an iterative-hard-thresholding algorithm when [Formula: see text] for some exponent [Formula: see text]. We show that this is feasible for [Formula: see text], and that the proposed analysis cannot cover the case [Formula: see text]. The precise value of the optimal exponent [Formula: see text] is the object of a question, raised but unresolved in this paper, about head projections for the jointly low-rank and bisparse structure. Some related questions are partially answered in passing. For rank-one measurements, we suggest on arcane grounds an iterative-hard-thresholding algorithm modified to exploit the nonstandard restricted isometry property obeyed by this type of measurements.

scholarly journals The convergence guarantee of the iterative hard thresholding algorithm with suboptimal feedbacks for large systems

2019 ◽  
Vol 98 ◽  
pp. 101-107
Author(s):  
Ningning Han ◽  
Shidong Li ◽  
Zhanjie Song
Keyword(s):  
Hard Thresholding ◽  
Iterative Hard Thresholding ◽  
Large Systems ◽  
Thresholding Algorithm
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