Applied Random Matrix Theory
Probability
2026-05-01 v1 Numerical Analysis
Numerical Analysis
Abstract
Random matrices now play a role in many parts of computational mathematics. To advance these applications, it is desirable to have tools that are flexible, easy to use, and powerful. Over the last 25 years, researchers have developed a remarkable family of results, called matrix concentration inequalities, that meet the criteria. This paper offers an invitation to the field of matrix concentration and its multifarious applications.
Cite
@article{arxiv.2604.27119,
title = {Applied Random Matrix Theory},
author = {Joel A. Tropp},
journal= {arXiv preprint arXiv:2604.27119},
year = {2026}
}
Comments
21 pages. Preprint of a 2026 ICM Proceedings survey, with minor emendations