Hybrid safe-strong rules for efficient optimization in Lasso-type problems

DOI10.1016/j.csda.2020.107063OpenAlexW3065811904MaRDI QIDQ830584

Tianbao Yang, Patrick Breheny, YaoHui Zeng

Publication date: 7 May 2021

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1704.08742

zbMATH Keywords

strong ruleslarge-scale sparse learningLasso screeningpathwise coordinate descent

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

Adaptive hybrid screening for efficient lasso optimization ⋮ A convex-Nonconvex strategy for grouped variable selection

Uses Software

• UCI-ml
• glmnet
• biglasso

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Least angle regression. (With discussion)
• The Lasso problem and uniqueness
• Efficient block-coordinate descent algorithms for the group Lasso
• Pathwise coordinate optimization
• Coordinate descent algorithms for lasso penalized regression
• The Group Lasso for Logistic Regression
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• Strong Rules for Discarding Predictors in Lasso-Type Problems
• Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
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