Alternating direction multiplier method for matrix \(l_{2,1}\)-norm optimization in multitask feature learning problems

DOI10.1155/2020/4864296zbMath1459.65084OpenAlexW3081372833MaRDI QIDQ2007113

Yu-Jie Wang, Yaping Hu, Liying Liu

Publication date: 12 October 2020

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2020/4864296

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Applications of mathematical programming (90C90) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

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• Sparse regression using mixed norms
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• A proximal alternating direction method for \(\ell_{2,1}\)-norm least squares problem in multi-task feature learning
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• THE BARZILAI AND BORWEIN GRADIENT METHOD WITH NONMONOTONE LINE SEARCH FOR NONSMOOTH CONVEX OPTIMIZATION PROBLEMS
• Alternating Direction Algorithms for $\ell_1$-Problems in Compressive Sensing
• The Barzilai and Borwein Gradient Method for the Large Scale Unconstrained Minimization Problem
• The Split Bregman Method for L1-Regularized Problems
• Two-Point Step Size Gradient Methods
• Local Linear Convergence of the Alternating Direction Method of Multipliers on Quadratic or Linear Programs

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