scientific article; zbMATH DE number 7306914
From MaRDI portal
Publication:5149027
Samuel Vaiter, Mathurin Massias, Joseph Salmon, Alexandre Gramfort
Publication date: 5 February 2021
Full work available at URL: https://arxiv.org/abs/1907.05830
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
convex optimizationextrapolationgeneralized linear modelsLassosparse logistic regressionscreening rulesworking sets
Related Items
Screening Rules and its Complexity for Active Set Identification ⋮ Local linear convergence of proximal coordinate descent algorithm ⋮ Unnamed Item
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Simple bounds for recovering low-complexity models
- A constraint selection technique for a class of linear programs
- A first-order primal-dual algorithm for convex problems with applications to imaging
- The Lasso problem and uniqueness
- Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
- Pathwise coordinate optimization
- A Fast Active Set Block Coordinate Descent Algorithm for $\ell_1$-Regularized Least Squares
- CVXPY: A Python-Embedded Modeling Language for Convex Optimization
- Square-root lasso: pivotal recovery of sparse signals via conic programming
- Fixed-Point Continuation for $\ell_1$-Minimization: Methodology and Convergence
- Accelerated, Parallel, and Proximal Coordinate Descent
- On Sparse Representations in Arbitrary Redundant Bases
- Model Consistency of Partly Smooth Regularizers
- Model selection with low complexity priors
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Gap Safe screening rules for sparsity enforcing penalties
- Nonlinear Optimization by Successive Linear Programming
- Safe Feature Elimination in Sparse Supervised Learning
- Techniques for Removing Nonbinding Constraints and Extraneous Variables from Linear Programming Problems
- Strong Rules for Discarding Predictors in Lasso-Type Problems
- Convex analysis and monotone operator theory in Hilbert spaces
- Convergence of a block coordinate descent method for nondifferentiable minimization