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Angang Cui

As seen in: MDPI
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Articles

A Designed Thresholding Operator for Low-Rank Matrix Completion

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By Angang Cui, Haizhen He
| MDPI
1. Introduction In recent years, the problem of recovering an unknown low-rank matrix from its limited number of observed entries has been actively studied in many scientific applications such as Netflix problem [1], image processing [2], system identification [3], video denoising [4], signal processing [5], subspace learning [6], and so on. In mathematics, this problem can be modeled by the following low-rank matrix completion problem: min X ∈ R m × n rank ( X ) s . t .
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The Sufficiency of Solutions for Non-smooth Minimax Fractional Semi-Infinite Programming with (BK,ρ)−Invexity

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By Angang Cui
| MDPI
Share and Cite MDPI and ACS Style Yang, H.; Cui, A. The Sufficiency of Solutions for Non-smooth Minimax Fractional Semi-Infinite Programming with (BK,ρ)−Invexity. Mathematics 2023, 11, 4240. https://doi.org/10.3390/math11204240 AMA Style Yang H, Cui A. The Sufficiency of Solutions for Non-smooth Minimax Fractional Semi-Infinite Programming with (BK,ρ)−Invexity. Mathematics. 2023; 11(20):4240. https://doi.org/10.3390/math11204240 Chicago/Turabian Style Yang, Hong, and Angang Cui. 2023.
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