The rank of sparse random matrices
Amin Coja-Oghlan, Alperen A. Ergür, Pu Gao, Samuel Hetterich, Maurice Rolvien
TL;DR
This work determines the asymptotic rank of sparse random matrices over arbitrary fields with prescribed row/column degrees, showing that $\frac{\rk(\boldsymbol{A})}{n} \to 1-\max_{\alpha\in[0,1]}\Phi(\alpha)$ in probability, where $\Phi$ depends only on the degree distributions via their generating functions $D$ and $K$. The authors introduce a novel perturbation $A[\theta]$ that eliminates short linear relations and couple matrix sizes through an Aizenman-Sims-Starr scheme to derive a rigorous lower bound matching the cavity-method prediction, while also providing an alternative upper bound via interpolation. A central insight is that the rank is governed by large-scale dependency structures encoded in the degree distributions, with the 2-core bound providing a sharp threshold in many, but not all, cases; tightness is established under several natural degree-structure regimes, and counterexamples are discussed. The results have direct implications for the rate of low-density parity-check codes and demonstrate the limits of non-rigorous cavity-method predictions in general degree distributions. Overall, the paper delivers a rigorous, field-independent rank formula for sparse random matrices and clarifies the landscape where the 2-core bound is or is not tight, with broad methodological impact on random factor graphs and coding theory.
Abstract
We determine the rank of a random matrix over an arbitrary field with prescribed numbers of non-zero entries in each row and column. As an application we obtain a formula for the rate of low-density parity check codes. This formula vindicates a conjecture of Lelarge (2013). The proofs are based on coupling arguments and a novel random perturbation, applicable to any matrix, that diminishes the number of short linear relations.
