Low Tucker rank tensor recovery via ADMM based on exact and inexact iteratively reweighted algorithms

DOI10.1016/j.cam.2017.09.029zbMath1377.65066OpenAlexW2763810251MaRDI QIDQ1678114

Yu-Fan Li, Kun Shang, Zheng-Hai Huang

Publication date: 14 November 2017

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.2017.09.029

zbMATH Keywords

numerical experimentalternative direction method of multiplierstensor completion\(L_p\) normiteratively reweighted algorithmslow Tucker rank tensor recoverynon-convex minimization problem

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Approximation methods and heuristics in mathematical programming (90C59)

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Cites Work

• Unnamed Item
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• Tensor Decompositions and Applications
• Parallel matrix factorization for low-rank tensor completion
• Learning with tensors: a framework based on convex optimization and spectral regularization
• A reweighted nuclear norm minimization algorithm for low rank matrix recovery
• Exact matrix completion via convex optimization
• Convergence Analysis of Alternating Direction Method of Multipliers for a Family of Nonconvex Problems
• Tensor completion and low-n-rank tensor recovery via convex optimization
• An Adaptive Correction Approach for Tensor Completion
• Global Convergence of Splitting Methods for Nonconvex Composite Optimization
• A Fixed Point Iterative Method for Low $n$-Rank Tensor Pursuit
• Convergence of alternating direction method for minimizing sum of two nonconvex functions with linear constraints
• A Splitting Augmented Lagrangian Method for Low Multilinear-Rank Tensor Recovery
• Alternating Direction Method of Multipliers for a Class of Nonconvex and Nonsmooth Problems with Applications to Background/Foreground Extraction
• The Power of Convex Relaxation: Near-Optimal Matrix Completion
• Restricted $p$-Isometry Properties of Nonconvex Matrix Recovery