Hybrid safe-strong rules for efficient optimization in Lasso-type problems
type: Item
instance of: scholarly article
title: Hybrid safe-strong rules for efficient optimization in Lasso-type problems
author: Tianbao Yang
author: Patrick Breheny
author: YaoHui Zeng
published in: Computational Statistics and Data Analysis
publication date: 2021-05-07T00:00:00Z
full work available at URL: https://arxiv.org/abs/1704.08742
Mathematics Subject Classification ID: 62-XX
zbMATH DE Number: 7345909
zbMATH Keywords: Lasso screening
zbMATH Keywords: strong rules
zbMATH Keywords: pathwise coordinate descent
zbMATH Keywords: large-scale sparse learning
describes a project that uses: UCI-ml
describes a project that uses: glmnet
describes a project that uses: biglasso
MaRDI profile type: Publication
OpenAlex ID: W3065811904
cites work: Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
cites work: Q4821526
cites work: Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
cites work: Least angle regression. (With discussion)
cites work: Sure Independence Screening for Ultrahigh Dimensional Feature Space
cites work: Pathwise coordinate optimization
cites work: The Group Lasso for Logistic Regression
cites work: Efficient block-coordinate descent algorithms for the group Lasso
cites work: Q5396661
cites work: Q4864293
cites work: The Lasso problem and uniqueness
cites work: Strong Rules for Discarding Predictors in Lasso-Type Problems
cites work: Q5744796
cites work: Coordinate descent algorithms for lasso penalized regression
cites work: Model Selection and Estimation in Regression with Grouped Variables
cites work: Regularization and Variable Selection Via the Elastic Net
DOI: 10.1016/J.CSDA.2020.107063
description: scientific article; zbMATH DE number 7345909