Sampled Tikhonov regularization for large linear inverse problems

DOI10.1088/1361-6420/ab2787zbMath1434.65083arXiv1812.06165OpenAlexW2981103608WikidataQ127751243 ScholiaQ127751243MaRDI QIDQ4973539

David Kozak, Julianne Chung, Luis Tenorio, J. Tanner Slagel, Matthias Chung

Publication date: 28 November 2019

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

Full work available at URL: https://arxiv.org/abs/1812.06165

zbMATH Keywords

recursive least squaresTikhonov regularizationimagingsampling methodsstreaming data

Mathematics Subject Classification ID

Ill-posedness and regularization problems in numerical linear algebra (65F22) Numerical optimization and variational techniques (65K10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Iterative numerical methods for linear systems (65F10)

Related Items (11)

An Optimal Scheduled Learning Rate for a Randomized Kaczmarz Algorithm ⋮ Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion ⋮ slimTrain---A Stochastic Approximation Method for Training Separable Deep Neural Networks ⋮ A relaxed iterated Tikhonov regularization for linear ill-posed inverse problems ⋮ A variable projection method for large-scale inverse problems with \(\ell^1\) regularization ⋮ Solution of the EEG inverse problem by random dipole sampling ⋮ Hybrid Projection Methods with Recycling for Inverse Problems ⋮ Effective new methods for automated parameter selection in regularized inverse problems ⋮ Hybrid Projection Methods with Recycling for Inverse Problems ⋮ Sampled limited memory methods for massive linear inverse problems ⋮ Non-contact detection of single-cell lead-acid battery electrodes’ defects through conductivity reconstruction by magnetic induction tomography

Uses Software

• LSQR
• Regularization tools
• SHARP
• CRAIG

Cites Work

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