Stochastic Gradient Descent Variants for Corrupted Systems of Linear Equations

Jamie Haddock, Deanna Needell, Elizaveta Rebrova, William Swartworth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2020 54th Annual Conference on Information Sciences and Systems, CISS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728140841
DOIs
StatePublished - Mar 2020
Externally publishedYes
Event54th Annual Conference on Information Sciences and Systems, CISS 2020 - Princeton, United States
Duration: Mar 18 2020Mar 20 2020

Publication series

Name2020 54th Annual Conference on Information Sciences and Systems, CISS 2020

Conference

Conference54th Annual Conference on Information Sciences and Systems, CISS 2020
Country/TerritoryUnited States
CityPrinceton
Period3/18/203/20/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence

Keywords

  • gradient methods
  • iterative methods
  • Linear systems

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