Singularity of sparse random matrices: simple proofs

TL;DR

This paper provides simple proofs that certain sparse random matrices are nonsingular with high probability, confirming a conjecture for the combinatorial model and strengthening known results for the Bernoulli model.

Contribution

It offers straightforward proofs establishing nonsingularity of sparse matrices in both Bernoulli and combinatorial models, resolving a conjecture in the combinatorial case.

Findings

Matrices are nonsingular with high probability if density exceeds (1+ε)log n/n

Confirms a conjecture for the combinatorial model

Simplifies proofs of known results in the Bernoulli model

Abstract

Consider a random $n\times n$ zero-one matrix with "density" $p$, sampled according to one of the following two models: either every entry is independently taken to be one with probability $p$ (the "Bernoulli" model), or each row is independently uniformly sampled from the set of all length-$n$ zero-one vectors with exactly $pn$ ones (the "combinatorial" model). We give simple proofs of the (essentially best-possible) fact that in both models, if $\min(p,1-p)\geq (1+\varepsilon)\log n/n$ for any constant $\varepsilon>0$, then our random matrix is nonsingular with probability $1-o(1)$. In the Bernoulli model this fact was already well-known, but in the combinatorial model this resolves a conjecture of Aigner-Horev and Person.