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A Novel Nonconvex Penalty Method for a Rank Constrained Matrix Optimization Problem and its Applications Cover

A Novel Nonconvex Penalty Method for a Rank Constrained Matrix Optimization Problem and its Applications

International Journal of Applied Mathematics and Computer Science
Volume 35 (2025): Issue 1 (March 2025)
By: Wenjuan Zhang,  Jiayi Yao,  Feng Xiao,  Yuping Wang and  Yulian Wu  
Open Access
|Apr 2025

Authors

Wenjuan Zhang

zhangwenjuan@xatu.edu.cn

School of Science Xi’an Technological, University Xi’an, Shaanxi, China

Jiayi Yao

School of Science Xi’an Technological, University Xi’an, Shaanxi, China

Feng Xiao

19456013@qq.com

School of Computer Science, Xi’an Technological University, Xi’an, China

Yuping Wang

School of Science Xi’an Technological, University Xi’an, Shaanxi, China

Yulian Wu

317903600@qq.com

Department of Health Management, Xi’an Medical University, Xi’an, China
DOI: https://doi.org/10.61822/amcs-2025-0012 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
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Language: English
Page range: 157 - 177
Published on: Apr 1, 2025
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
low rank,
penalty method,
nonconvex optimization,
nonsmooth optimization,
Bregman proximal gradient
Related subjects:
Mathematics,
Applied mathematics

© 2025 Wenjuan Zhang, Jiayi Yao, Feng Xiao, Yuping Wang, Yulian Wu, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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