An algorithm for matrix recovery of high-loss-rate network traffic data

DOI10.1016/j.apm.2021.03.036zbMath1481.90092OpenAlexW3137970382MaRDI QIDQ2243486

Mayank Bakshi, Yanwei Xu, Zhenyu Ming, Li-ping Zhang

Publication date: 11 November 2021

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2021.03.036

zbMATH Keywords

cross validationalternating direction method of multipliersnuclear normnetwork traffic

Mathematics Subject Classification ID

Convex programming (90C25) Applications of mathematical programming (90C90) Deterministic network models in operations research (90B10) Traffic problems in operations research (90B20)

Related Items

Low rank matrix minimization with a truncated difference of nuclear norm and Frobenius norm regularization ⋮ T-product factorization method for internet traffic data completion with spatio-temporal regularization

Cites Work

• Parallel matrix factorization for low-rank tensor completion
• An efficient inexact symmetric Gauss-Seidel based majorized ADMM for high-dimensional convex composite conic programming
• On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming
• A block symmetric Gauss-Seidel decomposition theorem for convex composite quadratic programming and its applications
• An Adaptive Correction Approach for Tensor Completion
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• Semidefinite Programming
• Compressed sensing
• The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent
• A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions