Adaptive Randomized Pivoting and Volume Sampling
Abstract
Abstract. Adaptive randomized pivoting (ARP) is a recently proposed and highly effective algorithm for column subset selection. This paper reinterprets the ARP algorithm by drawing connections to the volume sampling distribution and active learning algorithms for linear regression. As consequences, this paper presents new analysis for the ARP algorithm and faster implementations using rejection sampling. Reproducibility of computational results. This paper has been awarded the “SIAM Reproducibility Badge: Code and data available” as a recognition that the authors have followed reproducibility principles valued by SIMAX and the scientific computing community. Code and data that allow readers to reproduce the results in this paper are available at https://github.com/eepperly/Adaptive-Randomized-Pivoting and in the supplementary materials ( code.zip [356KB]). [Formula: see text]
// Source
Authors: Ethan N. Epperly
Institutions: University of California, Berkeley