Double fused Lasso penalized LAD for matrix regression

DOI10.1016/j.amc.2019.03.051zbMath1428.62319OpenAlexW2940420277WikidataQ128083089 ScholiaQ128083089MaRDI QIDQ2009580

Lingchen Kong, Mei Li

Publication date: 29 November 2019

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2019.03.051

zbMATH Keywords

LADmatrix regressionQ-linear rate of convergencedouble fused LassosGS-ADMM

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (2)

Safe feature screening rules for the regularized Huber regression ⋮ The linearized alternating direction method of multipliers for low-rank and fused LASSO matrix regression model

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• The \(L_1\) penalized LAD estimator for high dimensional linear regression
• An efficient inexact symmetric Gauss-Seidel based majorized ADMM for high-dimensional convex composite conic programming
• Estimation of (near) low-rank matrices with noise and high-dimensional scaling
• Robinson's implicit function theorem and its extensions
• Linearized alternating direction method of multipliers for sparse group and fused Lasso models
• A unified approach to error bounds for structured convex optimization problems
• Support union recovery in high-dimensional multivariate regression
• Hankel Matrix Rank Minimization with Applications to System Identification and Realization
• Variational Analysis
• On Efficiently Solving the Subproblems of a Level-Set Method for Fused Lasso Problems
• A Highly Efficient Semismooth Newton Augmented Lagrangian Method for Solving Lasso Problems
• Sparsity and Smoothness Via the Fused Lasso
• Dimension Reduction and Coefficient Estimation in Multivariate Linear Regression
• Regularized Matrix Regression
• Linear Rate Convergence of the Alternating Direction Method of Multipliers for Convex Composite Programming
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent

This page was built for publication: Double fused Lasso penalized LAD for matrix regression