An Adaptive Correction Approach for Tensor Completion

DOI10.1137/15M1048008zbMath1456.90104OpenAlexW2514832728MaRDI QIDQ3179604

Guyan Ni, Chunfeng Cui, Xiong-Jun Zhang, Min-Ru Bai

Publication date: 19 December 2016

Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/15m1048008

zbMATH Keywords

error boundtensor completion3-block ADMMadaptive correction approachsquare deal

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Nonlinear programming (90C30) Numerical methods of relaxation type (49M20)

Related Items (13)

Low Tucker rank tensor recovery via ADMM based on exact and inexact iteratively reweighted algorithms ⋮ A block symmetric Gauss-Seidel decomposition theorem for convex composite quadratic programming and its applications ⋮ A Learnable Group-Tube Transform Induced Tensor Nuclear Norm and Its Application for Tensor Completion ⋮ A Corrected Tensor Nuclear Norm Minimization Method for Noisy Low-Rank Tensor Completion ⋮ T-product factorization based method for matrix and tensor completion problems ⋮ Iterative \(p\)-shrinkage thresholding algorithm for low Tucker rank tensor recovery ⋮ Nonconvex optimization for robust tensor completion from grossly sparse observations ⋮ On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming ⋮ A multi-stage convex relaxation approach to noisy structured low-rank matrix recovery ⋮ An algorithm for matrix recovery of high-loss-rate network traffic data ⋮ Low-rank tensor completion based on log-det rank approximation and matrix factorization ⋮ Nonlocal robust tensor recovery with nonconvex regularization ^* ⋮ Robust Tensor Completion: Equivalent Surrogates, Error Bounds, and Algorithms

Cites Work

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• Tensor completion and low-n-rank tensor recovery via convex optimization
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• A Convergent 3-Block SemiProximal Alternating Direction Method of Multipliers for Conic Programming with 4-Type Constraints
• Most Tensor Problems Are NP-Hard
• Convex Analysis
• The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent

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