Effects of Data Heterogeneity on Distributed Linear System Solvers

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Velasevic, Boris

Advisor dc:contributor.advisor

• Azizan, Navid

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/157014

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/157014