Sparse regularization on thin grids I: the Lasso

DOI10.1088/1361-6420/aa5e12zbMath1373.65039OpenAlexW2585868105MaRDI QIDQ5268474

Publication date: 20 June 2017

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1088/1361-6420/aa5e12

zbMATH Keywords

stability inverse problem regularization Hilbert space linear operator equation Lasso Dirac masses deconvolution problem compressed sensing recovery

Mathematics Subject Classification ID

Numerical solutions to equations with linear operators (65J10) Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20) Linear operators and ill-posed problems, regularization (47A52) Numerical solution to inverse problems in abstract spaces (65J22)

Related Items (16)

Nonsmoothness in machine learning: specific structure, proximal identification, and applications ⋮ On the uniqueness of solutions for the basis pursuit in the continuum ⋮ Degrees of freedom for off-the-grid sparse estimation ⋮ ``FISTA in Banach spaces with adaptive discretisations ⋮ Smooth over-parameterized solvers for non-smooth structured optimization ⋮ The geometry of off-the-grid compressed sensing ⋮ Sparse spikes super-resolution on thin grids II: the continuous basis pursuit ⋮ TV-based reconstruction of periodic functions ⋮ MultiDimensional Sparse Super-Resolution ⋮ Multi-kernel unmixing and super-resolution using the modified matrix pencil method ⋮ The sliding Frank–Wolfe algorithm and its application to super-resolution microscopy ⋮ Modern regularization methods for inverse problems ⋮ Super-resolution for doubly-dispersive channel estimation ⋮ Sparsest piecewise-linear regression of one-dimensional data ⋮ The basins of attraction of the global minimizers of the non-convex sparse spike estimation problem ⋮ TV-based spline reconstruction with Fourier measurements: uniqueness and convergence of grid-based methods

Uses Software

PDCO

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