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SINGULARITY OF RANDOM SYMMETRIC MATRICES—A COMBINATORIAL APPROACH TO IMPROVED BOUNDS

Part of: Probability theory on algebraic and topological structures

Published online by Cambridge University Press:  02 August 2019

ASAF FERBER  and
VISHESH JAIN
ASAF FERBER
Affiliation:
Massachusetts Institute of Technology, Department of Mathematics, USA; ferbera@mit.edu, visheshj@mit.edu
VISHESH JAIN
Affiliation:
Massachusetts Institute of Technology, Department of Mathematics, USA; ferbera@mit.edu, visheshj@mit.edu

Abstract

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Let $M_{n}$ denote a random symmetric $n\times n$ matrix whose upper-diagonal entries are independent and identically distributed Bernoulli random variables (which take values $1$ and $-1$ with probability $1/2$ each). It is widely conjectured that $M_{n}$ is singular with probability at most $(2+o(1))^{-n}$. On the other hand, the best known upper bound on the singularity probability of $M_{n}$, due to Vershynin (2011), is $2^{-n^{c}}$, for some unspecified small constant $c>0$. This improves on a polynomial singularity bound due to Costello, Tao, and Vu (2005), and a bound of Nguyen (2011) showing that the singularity probability decays faster than any polynomial. In this paper, improving on all previous results, we show that the probability of singularity of $M_{n}$ is at most $2^{-n^{1/4}\sqrt{\log n}/1000}$ for all sufficiently large $n$. The proof utilizes and extends a novel combinatorial approach to discrete random matrix theory, which has been recently introduced by the authors together with Luh and Samotij.

MSC classification

Primary: 60B20: Random matrices (probabilistic aspects; for algebraic aspects see )

Information

Type
Research Article
Information
Forum of Mathematics, Sigma , Volume 7 , 2019 , e22
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s) 2019

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