On sampling Kaczmarz-Motzkin methods for solving large-scale nonlinear systems

DOI10.1007/s40314-023-02265-2OpenAlexW4360608645MaRDI QIDQ2695695

Qin Wang, Wen-Di Bao, Weiguo Li, Fei-Yu Zhang

Publication date: 31 March 2023

Published in: Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2209.04195

zbMATH Keywords

projection methodsampling Kaczmarz-Motzkin methodfinite convex constraintslarge-scale nonlinear equationsrandomized accelerated projection method

Mathematics Subject Classification ID

Numerical solutions to overdetermined systems, pseudoinverses (65F20) Numerical computation of solutions to systems of equations (65H10) Numerical solutions of ill-posed problems in abstract spaces; regularization (65J20)

Uses Software

• SparseMatrix

Cites Work

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• Nonlinear Kaczmarz algorithms and their convergence
• Kaczmarz methods for regularizing nonlinear ill-posed equations. I: Convergence analysis
• The university of Florida sparse matrix collection
• Quasi-Newton Methods, Motivation and Theory
• A Globally and Superlinearly Convergent Gauss--Newton-Based BFGS Method for Symmetric Nonlinear Equations
• On Projection Algorithms for Solving Convex Feasibility Problems
• Convex Analysis for Optimization
• On the Convergence of Stochastic Gradient Descent for Nonlinear Ill-Posed Problems
• A Sampling Kaczmarz--Motzkin Algorithm for Linear Feasibility

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