Abstract by Bryce Pierson
Reinforcement Learning and Gershgorin's Theorem
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.