A conjugate gradient like method for \(p\)-norm minimization in functional spaces

DOI10.1007/s00211-017-0893-7zbMath1379.65029OpenAlexW2621986455MaRDI QIDQ1681792

David Titley-Peloquin, Serge Gratton, Flavia Lenti, Claudio Estatico

Publication date: 24 November 2017

Published in: Numerische Mathematik (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00211-017-0893-7

zbMATH Keywords

iterative algorithmconjugate gradient methoddiscrepancy principleregularization methodnumerical resultminimum \(p\)-norm solution, Banach space

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)

Related Items (12)

A regularizing multilevel approach for nonlinear inverse problems ⋮ Generalized cross validation for \(\ell^p\)-\(\ell^q\) minimization ⋮ Graph Laplacian for image deblurring ⋮ Dual descent regularization algorithms in variable exponent Lebesgue spaces for imaging ⋮ A non-linear conjugate gradient in dual space for \(L_p\)-norm regularized non-linear least squares with application in data assimilation ⋮ Modulus-based iterative methods for constrained ℓ _p – ℓ _q minimization ⋮ Iterative methods for the split feasibility problem and the fixed point problem in Banach spaces ⋮ An \(\ell^2\)-\(\ell^q\) regularization method for large discrete ill-posed problems ⋮ A self-adaptive projection method with an inertial technique for split feasibility problems in Banach spaces with applications to image restoration problems ⋮ Irregularization accelerates iterative regularization ⋮ An \(\ell^p\)-\(\ell^q\) minimization method with cross-validation for the restoration of impulse noise contaminated images ⋮ Fast alternating direction multipliers method by generalized Krylov subspaces

Uses Software

• Regularization tools
• UTV

Cites Work

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