A generalized nonconvex algorithm framework for low-rank and sparse matrix decomposition

The low-rank and sparse matrix decomposition problem is a hot and challenging problem in computer science. In this paper, we consider it as a nonconvex relaxation optimization problem by using a family of nonconvex functions to approximate the rank function and the -norm in low-rank and sparse matri...

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Bibliographic Details
Published in:	Applied intelligence (Dordrecht, Netherlands) Vol. 55; no. 16; p. 1085
Main Authors:	Cui, Angang, Zhang, Lijun, He, Haizhen, Xue, Shengli
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.11.2025 Springer Nature B.V
Subjects:	Adaptive algorithms Algorithms Approximation Artificial Intelligence Computer Science Decomposition Machines Manufacturing Mechanical Engineering Optimization Parameters Processes Regularization Sparse matrices Sparsity Nonconvex algorithm framework Nonconvex function regularization Reneralized low-rank and sparse matrix decomposition problem Low-rank and sparse matrix decomposition
ISSN:	0924-669X, 1573-7497
Online Access:	Get full text
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