A fast proximal iteratively reweighted nuclear norm algorithm for nonconvex low-rank matrix minimization problems-Reference-Cited by-同舟云学术

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A fast proximal iteratively reweighted nuclear norm algorithm for nonconvex low-rank matrix minimization problems

Published:2022-09 Issue: Volume:179 Page:66-86
ISSN:0168-9274
Container-title:Applied Numerical Mathematics
language:en
Short-container-title:Applied Numerical Mathematics

Author:

Ge Zhili,Zhang Xin,Wu Zhongming

Funder

National Natural Science Foundation of China

Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Mathematics,Numerical Analysis

Reference46 articles.

1. Proximal alternating minimization and projection methods for nonconvex problems: an approach based on the Kurdyka-Łojasiewicz inequality;Attouch;Math. Oper. Res.,2010

2. Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods;Attouch;Math. Program.,2013

3. A fast iterative shrinkage-thresholding algorithm for linear inverse problems;Beck;SIAM J. Imaging Sci.,2009

4. Proximal alternating linearized minimization for nonconvex and nonsmooth problems;Bolte;Math. Program.,2014

5. An inertial forward-backward algorithm for the minimization of the sum of two nonconvex functions;Bot;EURO J. Comput. Optim.,2016

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1. An accelerated stochastic ADMM for nonconvex and nonsmooth finite-sum optimization;Automatica;2024-05

2. Smoothing fast proximal gradient algorithm for the relaxation of matrix rank regularization problem;Applied Numerical Mathematics;2023-08

3. An extrapolated proximal iteratively reweighted method for nonconvex composite optimization problems;Journal of Global Optimization;2023-06-07

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