Learning Theory of Randomized Sparse Kaczmarz Method-Reference-Cited by-同舟云学术

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Learning Theory of Randomized Sparse Kaczmarz Method

Published:2018-01 Issue:1 Volume:11 Page:547-574
ISSN:1936-4954
Container-title:SIAM Journal on Imaging Sciences
language:en
Short-container-title:SIAM J. Imaging Sci.

Author:

Lei Yunwen,Zhou Ding-Xuan

Funder

National Natural Science Foundation of China

Research Grants Council, University Grants Committee

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,General Mathematics

Link

https://epubs.siam.org/doi/pdf/10.1137/17M1136225

Reference26 articles.

1. On Projection Algorithms for Solving Convex Feasibility Problems

2. Bregman Monotone Optimization Algorithms

3. Strong conical hull intersection property, bounded linear regularity, Jameson's property (G), and error bounds in convex optimization

4. Convergence of the linearized Bregman iteration for ℓ₁-norm minimization

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1. The sparse Kaczmarz method with surrogate hyperplane for the regularized basis pursuit problem;Journal of Computational and Applied Mathematics;2025-01

2. A Note on the Randomized Kaczmarz Algorithm for Solving Doubly Noisy Linear Systems;SIAM Journal on Matrix Analysis and Applications;2024-05-06

3. On convergence rates of Kaczmarz-type methods with different selection rules of working rows;Applied Numerical Mathematics;2023-04

4. A semi-randomized Kaczmarz method with simple random sampling for large-scale linear systems;Advances in Computational Mathematics;2023-03-10

5. THE GREEDY RANDOMIZED EXTENDED KACZMARZ ALGORITHM FOR NOISY LINEAR SYSTEMS;Journal of Applied Analysis & Computation;2023

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