On proximal gradient method for the convex problems regularized with the group reproducing kernel norm

DOI10.1007/s10898-013-0034-5zbMath1318.90055OpenAlexW2144587591MaRDI QIDQ2442634

Hai-Bin Zhang, Juan Wei, Jie Zhou, Meixia Li, Mian-Tao Chao

Publication date: 1 April 2014

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-013-0034-5

zbMATH Keywords

linear convergencegroup Lassononsmooth convex optimizationproximal gradient methodquadratical convergence

Mathematics Subject Classification ID

Convex programming (90C25) Methods of reduced gradient type (90C52)

Related Items

On globally Q-linear convergence of a splitting method for group Lasso ⋮ A modified proximal gradient method for a family of nonsmooth convex optimization problems ⋮ Proximal gradient method with automatic selection of the parameter by automatic differentiation ⋮ On the proximal Landweber Newton method for a class of nonsmooth convex problems

Uses Software

• UNLocBoX
• SLEP

Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• Adaptive cubic regularisation methods for unconstrained optimization. I: Motivation, convergence and numerical results
• Approximation accuracy, gradient methods, and error bound for structured convex optimization
• Four parameter proximal point algorithms
• Inexact Halpern-type proximal point algorithm
• A coordinate gradient descent method for nonsmooth separable minimization
• On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
• Introductory lectures on convex optimization. A basic course.
• Proximal Splitting Methods in Signal Processing
• The Group Lasso for Logistic Regression
• Projected gradient methods for linearly constrained problems
• On the Linear Convergence of Descent Methods for Convex Essentially Smooth Minimization
• Variational Analysis
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparse Reconstruction by Separable Approximation
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• Signal Recovery by Proximal Forward-Backward Splitting
• Convex Analysis