Linearly convergent adjoint free solution of least squares problems by random descent

DOI10.1088/1361-6420/ad08edarXiv2306.01946OpenAlexW4388210167MaRDI QIDQ6141569

Lionel Tondji, Felix Schneppe, Dirk A. Lorenz

Publication date: 20 December 2023

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

Full work available at URL: https://arxiv.org/abs/2306.01946

zbMATH Keywords

least squares problemsrandomized algorithmsstochastic gradient descent

Mathematics Subject Classification ID

Convex programming (90C25) Monte Carlo methods (65C05) Iterative numerical methods for linear systems (65F10) Randomized algorithms (68W20)

Cites Work

• A Monte Carlo method for solving unsteady adjoint equations
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• Randomized Iterative Methods for Linear Systems
• CGS, A Fast Lanczos-Type Solver for Nonsymmetric Linear systems
• Preasymptotic convergence of randomized Kaczmarz method
• High-Dimensional Probability
• Randomized Kaczmarz Converges Along Small Singular Vectors
• A Transpose-Free Quasi-Minimal Residual Algorithm for Non-Hermitian Linear Systems
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