BYU

Abstract by Bryce Pierson

Personal Infomation


Presenter's Name

Bryce Pierson

Co-Presenters

None

Degree Level

Undergraduate

Co-Authors

None

Abstract Infomation


Department

Mathematics

Faculty Advisor

Mark Hughes

Title

Reinforcement Learning and Gershgorin's Theorem

Abstract

From principle component analysis to graph theory, and computer science to geology, scientists and mathematicians have numerous uses for eigenvalues, and are extremely interested in the calculation and approximation of these values.  In this talk I will describe an approach to studying Gershgorin's theorem, which gives a method of approximating eigenvalues, through the lens of machine learning.  I will discuss techniques from  reinforcement learning which can be used to better understand Gershgorin's theorem, and show how these techniques can be used to find efficient isospectral reductions of matrices.