An Introduction to Matrix Concentration Inequalities
Probability
2015-01-08 v1 Data Structures and Algorithms
Information Theory
Numerical Analysis
math.IT
Machine Learning
Abstract
In recent years, random matrices have come to play a major role in computational mathematics, but most of the classical areas of random matrix theory remain the province of experts. Over the last decade, with the advent of matrix concentration inequalities, research has advanced to the point where we can conquer many (formerly) challenging problems with a page or two of arithmetic. The aim of this monograph is to describe the most successful methods from this area along with some interesting examples that these techniques can illuminate.
Cite
@article{arxiv.1501.01571,
title = {An Introduction to Matrix Concentration Inequalities},
author = {Joel A. Tropp},
journal= {arXiv preprint arXiv:1501.01571},
year = {2015}
}
Comments
163 pages. To appear in Foundations and Trends in Machine Learning