Speaker
Description
The column subset selection problem seeks to find a collection of the matrix columns that have similar spectral properties to the original matrix. Recently with the large amount of data available, many have turned to using randomization to reduce the problem's computation. While there have been many methods that motivate how to select these columns, they are just that--individual columns. However, by blocking these columns, there is less computational communication needed, which makes the process faster. In this talk we will discuss optimality conditions for selecting these block of columns using randomization. We relate the worst case of this randomized method to the deterministic block QR with column pivoting (QRCP). We then corroborate this analysis with numerical experiments to showcase the method's speed and accuracy compared to standard QRCP algorithms.