Non-asymptotic theory of random matrices: extreme singular values
Functional Analysis
2014-03-05 v2 Probability
Abstract
The classical random matrix theory is mostly focused on asymptotic spectral properties of random matrices as their dimensions grow to infinity. At the same time many recent applications from convex geometry to functional analysis to information theory operate with random matrices in fixed dimensions. This survey addresses the non-asymptotic theory of extreme singular values of random matrices with independent entries. We focus on recently developed geometric methods for estimating the hard edge of random matrices (the smallest singular value).
Keywords
Cite
@article{arxiv.1003.2990,
title = {Non-asymptotic theory of random matrices: extreme singular values},
author = {Mark Rudelson and Roman Vershynin},
journal= {arXiv preprint arXiv:1003.2990},
year = {2014}
}
Comments
Submission for ICM 2010. Some typographic corrections made