List Recovery for Random Low-Rate Linear Codes
It provides a nearly optimal list recovery guarantee for random low-rate linear codes, addressing a fundamental question in coding theory.
The paper proves that random low-rate linear codes over large prime fields are list recoverable for extremely large input list sizes, nearly matching a lower bound that shows any linear code of dimension at least two cannot achieve such list recovery for significantly larger list sizes.
We prove a list recovery guarantee for random low-rate linear codes over sufficiently large prime fields. For fixed dimension $d$, error fraction $α$, and accuracy parameter $\varepsilon$, a random $d$-dimensional linear code $C \subseteq \mathbb{F}_p^n$ is, with high probability, $(α,\ell,\frac{1+\varepsilon}{1-α}\ell)$-list recoverable simultaneously for all input list sizes $\ell\le 2^{O_{α, \varepsilon, d}(n/\log n)}$. The proof is inspired by work of Matoušek, Př\'ıvětivý, and Škovroň on reconstructing point sets from their projections. It combines a deterministic graph-theoretic certificate, a nonvanishing determinant criterion, and the Schwartz--Zippel lemma. We also give a lower bound showing that any linear code $C \subseteq \mathbb{F}_p^n$ of dimension at least two cannot be $(α,\ell,\frac{1+\varepsilon}{1-α}\ell)$-list recoverable for feasible list sizes $\ell \geq 2^{Ω_{α, \varepsilon}(n)}$. In this sense, our result is nearly optimal.