Thresholding gradient methods in Hilbert spaces: support identification and linear convergence

DOI10.1051/cocv/2019011OpenAlexW2963991544MaRDI QIDQ5109200

Silvia Villa, Lorenzo Rosasco, Guillaume Garrigos

Publication date: 11 May 2020

Published in: ESAIM: Control, Optimisation and Calculus of Variations (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1712.00357

zbMATH Keywords

convergence ratesconditioningforward-backward methodsupport identification

Mathematics Subject Classification ID

Convex programming (90C25) Optimality conditions and duality in mathematical programming (90C46) Numerical solutions to equations with linear operators (65J10) Numerical solutions to equations with nonlinear operators (65J15) Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20) Numerical solution to inverse problems in abstract spaces (65J22) Numerical methods for variational inequalities and related problems (65K15)

Related Items (6)

Nonsmoothness in machine learning: specific structure, proximal identification, and applications ⋮ On the strong convergence of forward-backward splitting in reconstructing jointly sparse signals ⋮ Quadratic growth conditions and uniqueness of optimal solution to Lasso ⋮ Convergence rates of gradient methods for convex optimization in the space of measures ⋮ Convergence of the forward-backward algorithm: beyond the worst-case with the help of geometry ⋮ On the linear convergence of forward-backward splitting method. I: Convergence analysis

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