An accelerated IRNN-iteratively reweighted nuclear norm algorithm for nonconvex nonsmooth low-rank minimization problems

DOI10.1016/j.cam.2021.113602zbMath1469.90114OpenAlexW3152703571MaRDI QIDQ2029679

Thuy Ngoc Nguyen, Duy Nhat Phan

Publication date: 3 June 2021

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2021.113602

zbMATH Keywords

matrix completionsubspace clusteringproximal gradient algorithmiteratively reweighted algorithmlow-rank minimizationNesterov's acceleration

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Methods of reduced gradient type (90C52)

Related Items (3)

A fast proximal iteratively reweighted nuclear norm algorithm for nonconvex low-rank matrix minimization problems ⋮ Low rank matrix recovery with impulsive noise ⋮ Low rank matrix minimization with a truncated difference of nuclear norm and Frobenius norm regularization

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