scholarly journals Matrix completion by singular value thresholding: Sharp bounds

2015 ◽  
Vol 9 (2) ◽  
pp. 2348-2369 ◽  
Author(s):  
Olga Klopp
Keyword(s):  
Matrix Completion ◽  
Singular Value ◽  
Sharp Bounds ◽  
Singular Value Thresholding

scholarly journals A Singular Value Thresholding Algorithm for Matrix Completion

2010 ◽  
Vol 20 (4) ◽  
pp. 1956-1982 ◽  
Author(s):  
Jian-Feng Cai ◽  
Emmanuel J. Candès ◽  
Zuowei Shen
Keyword(s):  
Matrix Completion ◽  
Singular Value ◽  
Thresholding Algorithm ◽  
Singular Value Thresholding

scholarly journals A Singular Value Thresholding with Diagonal-Update Algorithm for Low-Rank Matrix Completion

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yong-Hong Duan ◽  
Rui-Ping Wen ◽  
Yun Xiao
Keyword(s):  
Matrix Completion ◽  
Arithmetic Operation ◽  
Computational Cost ◽  
Singular Value ◽  
Low Rank ◽  
Simple Arithmetic ◽  
Rank Matrix ◽  
Low Rank Matrix Completion ◽  
Low Rank Matrix ◽  
Singular Value Thresholding

The singular value thresholding (SVT) algorithm plays an important role in the well-known matrix reconstruction problem, and it has many applications in computer vision and recommendation systems. In this paper, an SVT with diagonal-update (D-SVT) algorithm was put forward, which allows the algorithm to make use of simple arithmetic operation and keep the computational cost of each iteration low. The low-rank matrix would be reconstructed well. The convergence of the new algorithm was discussed in detail. Finally, the numerical experiments show the effectiveness of the new algorithm for low-rank matrix completion.

scholarly journals An optimized 2D-Robust Adaptive Beamforming algorithm based on Matrix Completion in sparse array

2018 ◽  
Vol 208 ◽  
pp. 01003
Author(s):  
Jiaying Di ◽  
Wen Hu ◽  
Mengxia Li ◽  
Hongtao Li
Keyword(s):  
Matrix Completion ◽  
Adaptive Beamforming ◽  
Singular Value ◽  
Spectral Norm ◽  
Sparse Array ◽  
Robust Adaptive Beamforming ◽  
Robust Adaptive ◽  
Singular Value Thresholding ◽  
Beamforming Algorithm ◽  
Norm Error

The sparse arrays can reduce the cost of beamforming, it greatly reduces the number of actual array elements. However, it also brings about the problem of information loss. A 2D-robust adaptive beamforming algorithm in sparse array based on Singular Value Thresholding algorithm is proposed. At first, a signal model of planar array is established based on Matrix Completion, which can be proved to meet Null Space Property. Then the Genetic Algorithm is used to optimize the sparse array, which is determined to reduce the Spectral Norm Error of Matrix Completion and make the array recovered closer to the full array. In the case of sparse array, the missing information is restored by using the theory of Singular Value Thresholding, and then the restored signal is used to design the digital beamformer weights. This algorithm significantly reduces the Spectral Norm Error and forms robust adaptive beam.

scholarly journals On Accelerated Singular Value Thresholding Algorithm for Matrix Completion

2014 ◽  
Vol 05 (21) ◽  
pp. 3445-3451 ◽  
Author(s):  
Li Wang ◽  
Jianfeng Hu ◽  
Chuanzhong Chen
Keyword(s):  
Matrix Completion ◽  
Singular Value ◽  
Thresholding Algorithm ◽  
Singular Value Thresholding

Anode effect prediction based on a singular value thresholding and extreme gradient boosting approach

2018 ◽  
Vol 30 (1) ◽  
pp. 015104 ◽  
Author(s):  
Kai-Bo Zhou ◽  
Zhi-Xin Zhang ◽  
Jie Liu ◽  
Zhong-Xu Hu ◽  
Xiao-Kun Duan ◽  
...  
Keyword(s):  
Singular Value ◽  
Anode Effect ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Singular Value Thresholding

scholarly journals Efficient quantum circuit for singular-value thresholding

2018 ◽  
Vol 98 (1) ◽  
Author(s):  
Bojia Duan ◽  
Jiabin Yuan ◽  
Ying Liu ◽  
Dan Li
Keyword(s):  
Quantum Circuit ◽  
Singular Value ◽  
Singular Value Thresholding
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