English

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.

Keywords

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

R2 v1 2026-06-22T07:53:59.620Z