Jigen Peng
Guangzhou University
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Publication
Featured researches published by Jigen Peng.
Journal of Computational and Applied Mathematics | 2018
Angang Cui; Jigen Peng; Haiyang Li; Chengyi Zhang; Yongchao Yu
Affine matrix rank minimization problem is a fundamental problem with a lot of important applications in many fields. It is well known that this problem is combinatorial and NP-hard in general. In this paper, a continuous promoting low rank non-convex fraction function is studied to replace the rank function in this NP-hard problem. Inspired by our former work in compressed sensing, an iterative singular value thresholding algorithm is proposed to solve the regularization transformed affine matrix rank minimization problem. For different
Journal of Computational and Applied Mathematics | 2019
Angang Cui; Jigen Peng; Haiyang Li; Meng Wen; Junxiong Jia
a>0
Archive | 2017
Haiyang Li; Qian Zhang; Angang Cui; Jigen Peng
, we could get a much better result by adjusting the different value of
arXiv: Optimization and Control | 2018
Angang Cui; Jigen Peng; Haiyang Li; Changlong Wang
a
arXiv: Optimization and Control | 2018
Angang Cui; Jigen Peng; Haiyang Li
, which is one of the advantages for the iterative singular value thresholding algorithm compared with some state-of-art methods. Some convergence results are established and numerical experiments show that this thresholding algorithm is feasible for solving the regularization transformed affine matrix rank minimization problem. Moreover, we proved that the value of the regularization parameter
arXiv: Optimization and Control | 2018
Angang Cui; Jigen Peng; Chengyi Zhang; Haiyang Li; Meng Wen
lambda>0
arXiv: Optimization and Control | 2018
Angang Cui; Haiyang Li; Jigen Peng; Junxiong Jia
can not be chosen too large. Indeed, there exists
arXiv: Optimization and Control | 2018
Angang Cui; Jigen Peng; Haiyang Li; Junxiong Jia; Meng Wen
bar{lambda}>0
Multidimensional Systems and Signal Processing | 2018
Meng Wen; Yu-Chao Tang; Angang Cui; Jigen Peng
such that the optimal solution of the regularization transformed affine matrix rank minimization problem is equal to zero for any
Journal of Functional Analysis | 2018
Junxiong Jia; Shigang Yue; Jigen Peng; Jinghuai Gao
lambda>bar{lambda}