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An upper bound on the smallest singular value of a square random matrix

Banff International Research Station for Mathematical Innovation and Discovery

Let $A = (a_{ij})$ be a square $n\times n$ matrix with i.i.d. zero mean and unit variance entries. In a paper by Rudelson and Vershynin it was shown that the upper bound for a smallest singular value $s_n(A)$ is of order $n^{-\frac12}$ with probability close to one under additional assumption on entries of $A$ that $\mathbb{E}a^4_{ij} < \infty$. We remove the assumption on the fourth moment and show the upper bound assuming only $\mathbb{E}a^2_{ij} = 1.$

Mathematics; Functional analysis; Convex and discrete geometry

video/mp4

Author affiliation: University of Alberta

2018-09-24

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/67237

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/