@inproceedings{a0ad15df36b34bb18865ca667ea096ce,
title = "Stochastic Gradient Descent Variants for Corrupted Systems of Linear Equations",
abstract = "Often in applications like medical imaging, error correction, and sensor networks, one needs to solve large-scale linear systems in which a fraction of the measurements have been corrupted. We consider solving such large-scale systems of linear equations Ax=b that are inconsistent due to corruptions in the measurement vector b. With this as our motivating example, we develop several variants of stochastic gradient descent that converge to the solution of the uncorrupted system of equations, even in the presence of large corruptions. We present both theoretical and empirical results that demonstrate the promise of these iterative methods.",
keywords = "Linear systems, gradient methods, iterative methods",
author = "Jamie Haddock and Deanna Needell and Elizaveta Rebrova and William Swartworth",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 54th Annual Conference on Information Sciences and Systems, CISS 2020 ; Conference date: 18-03-2020 Through 20-03-2020",
year = "2020",
month = mar,
doi = "10.1109/CISS48834.2020.1570631711",
language = "English (US)",
series = "2020 54th Annual Conference on Information Sciences and Systems, CISS 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 54th Annual Conference on Information Sciences and Systems, CISS 2020",
address = "United States",
}