Recent developments in non-asymptotic theory of random matrices
Probability
2013-08-02 v2 Functional Analysis
Abstract
Non-asymptotic theory of random matrices strives to investigate the spectral properties of random matrices, which are valid with high probability for matrices of a large fixed size. Results obtained in this framework find their applications in high-dimensional convexity, analysis of convergence of algorithms, as well as in random matrix theory itself. In these notes we survey some recent results in this area and describe the techniques aimed for obtaining explicit probability bounds.
Cite
@article{arxiv.1301.2382,
title = {Recent developments in non-asymptotic theory of random matrices},
author = {Mark Rudelson},
journal= {arXiv preprint arXiv:1301.2382},
year = {2013}
}
Comments
Lecture notes from the AMS short course on random matrices, 39 pages