Alternating direction method of multipliers for generalized low-rank tensor recovery

DOI10.3390/a9020028zbMath1461.94046OpenAlexW2341988032MaRDI QIDQ1736785

Wei Yang, Xiuyun Zheng, Jiarong Shi, Qingyan Yin

Publication date: 26 March 2019

Published in: Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3390/a9020028

zbMATH Keywords

alternating direction method of multiplierslow-rank matrix recoverynuclear norm minimizationlow-rank tensor recovery

Mathematics Subject Classification ID

Nonlinear programming (90C30) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Multilinear algebra, tensor calculus (15A69) Numerical linear algebra (65F99)

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Tensor Decompositions and Applications
• Low-rank tensor completion by Riemannian optimization
• A fast tri-factorization method for low-rank matrix recovery and completion
• An efficient matrix bi-factorization alternative optimization method for low-rank matrix recovery and completion
• Tensor-based techniques for the blind separation of DS-CDMA signals
• Exact matrix completion via convex optimization
• Robust Low-Rank Tensor Recovery: Models and Algorithms
• Robust principal component analysis?
• A Singular Value Thresholding Algorithm for Matrix Completion
• Tensor completion and low-n-rank tensor recovery via convex optimization
• Monotone Operators and the Proximal Point Algorithm
• Robust PCA via Outlier Pursuit