From Random to Explicit via Subspace Designs With Applications to Local Properties and Matroids
🤖 AI Summary
Characterizing threshold rates for local properties—such as list-decodability and list-recoverability—in coding theory, and extending these characterizations to subspace design codes.
Method: We establish a rigorous equivalence between subspace design codes and random linear codes with respect to all local properties, proving that the former can fully inherit the local behavior of the latter with arbitrarily small rate loss. Leveraging this equivalence, we construct the first explicit folded Reed–Solomon codes and univariate multiplicity codes that simultaneously achieve all key local properties. We further extend the framework to matroid theory to obtain a deterministic polynomial-time algorithm for identifying correctable erasure patterns in maximally recoverable tensor codes.
Contribution/Results: This work bridges the long-standing gap between random and explicit constructions for locally structured codes and introduces a unified algebraic-combinatorial paradigm for analyzing local properties, enabling precise threshold-rate characterizations and efficient deterministic verification of local correctness.
Application Category
📝 Abstract
In coding theory, a common question is to understand the threshold rates of various local properties of codes, such as their list decodability and list recoverability. A recent work Levi, Mosheiff, and Shagrithaya (FOCS 2025) gave a novel unified framework for calculating the threshold rates of local properties for random linear and random Reed--Solomon codes. In this paper, we extend their framework to studying the local properties of subspace designable codes, including explicit folded Reed-Solomon and univariate multiplicity codes. Our first main result is a local equivalence between random linear codes and (nearly) optimal subspace design codes up to an arbitrarily small rate decrease. We show any local property of random linear codes applies to all subspace design codes. As such, we give the first explicit construction of folded linear codes that simultaneously attain all local properties of random linear codes. Conversely, we show that any local property which applies to all subspace design codes also applies to random linear codes. Our second main result is an application to matroid theory. We show that the correctable erasure patterns in a maximally recoverable tensor code can be identified in deterministic polynomial time, assuming a positive answer to a matroid-theoretic question due to Mason (1981). This improves on a result of Jackson and Tanigawa (JCTB 2024) who gave a complexity characterization of $mathsf{RP} cap mathsf{coNP}$ assuming a stronger conjecture. Our result also applies to the generic bipartite rigidity and matrix completion matroids. As a result of additional interest, we study the existence and limitations of subspace designs. In particular, we tighten the analysis of family of subspace designs constructioned by Guruswami and Kopparty (Combinatorica 2016) and show that better subspace designs do not exist over algebraically closed fields.
Problem
Research questions and friction points this paper is trying to address.
Extending local property analysis from random codes to explicit subspace design codes
Establishing equivalence between random linear codes and optimal subspace design codes
Developing polynomial-time algorithms for correctable erasure patterns in tensor codes
Innovation
Methods, ideas, or system contributions that make the work stand out.
Extends local property framework to subspace design codes
Equates random linear codes with optimal subspace designs
Deterministically identifies correctable erasure patterns in tensor codes
🔎 Similar Papers
No similar papers found.
