Effects of Data Heterogeneity on Distributed Linear System Solvers
Name
velasevic-borisvel-meng-eecs-2024-thesis.pdf
Description
Thesis PDF
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1.07 MB
Format
Adobe PDF
Checksum (MD5)
62f98509c1217aba05e90cbaed32a5e3
Author(s)
Velasevic, Boris
Advisor(s)
Azizan, Navid
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
Abstract
We focus on the fundamental problem of solving a system of linear equations. In particular, we are interested in distributed linear system solvers, where one taskmaster coordinates any number of workers to attain a solution. There are two predominant and fundamentally different ways of doing this: optimization-based and projection-based solvers. Although there is extensive literature on both classes of algorithms, a rigorous analytical comparison of their performance is lacking. Consequently, there is no concrete understanding of why numerical experiments show that projection-based solvers tend to perform better in many real and synthetic scenarios. In this work, we develop a framework for such analysis, and we use that framework to investigate the comparison of optimization-based and projection-based solvers.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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