Rahul Jain
EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2022-52
May 10, 2022
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-52.pdf
In the past, algorithms for solving linear systems of equations have focused on finding a solution that is not only stable with respect to small changes to the input, but with a very small error with respect to the analytical solution. However, this comes at the cost of an increased runtime. There has been an increased need to find a solution to linear system in a small amount of time, requiring modest accuracy. Randomized algorithms are quite beneficial in this aspect in that they can have a smaller runtime than their deterministic counterparts. In this thesis, we explore and then modify Randomized Block Kaczmarz, a randomized algorithm for solving overdetermined linear systems of equations, to see how practically effective it can be.
Advisor: James Demmel
BibTeX citation:
@mastersthesis{Jain:EECS-2022-52, Author = {Jain, Rahul}, Title = {Tuning Doubly Randomized Block Kaczmarz Method}, School = {EECS Department, University of California, Berkeley}, Year = {2022}, Month = {May}, URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-52.html}, Number = {UCB/EECS-2022-52}, Abstract = {In the past, algorithms for solving linear systems of equations have focused on finding a solution that is not only stable with respect to small changes to the input, but with a very small error with respect to the analytical solution. However, this comes at the cost of an increased runtime. There has been an increased need to find a solution to linear system in a small amount of time, requiring modest accuracy. Randomized algorithms are quite beneficial in this aspect in that they can have a smaller runtime than their deterministic counterparts. In this thesis, we explore and then modify Randomized Block Kaczmarz, a randomized algorithm for solving overdetermined linear systems of equations, to see how practically effective it can be.} }
EndNote citation:
%0 Thesis %A Jain, Rahul %T Tuning Doubly Randomized Block Kaczmarz Method %I EECS Department, University of California, Berkeley %D 2022 %8 May 10 %@ UCB/EECS-2022-52 %U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2022/EECS-2022-52.html %F Jain:EECS-2022-52