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Average-radius list-recoverability of random linear codes

  • Stanford University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

35 Scopus citations

Abstract

We analyze the list-decodability, and related notions, of random linear codes. This has been studied extensively before: there are many different parameter regimes and many different variants. Previous works have used complementary styles of arguments|which each work in their own parameter regimes but not in others| and moreover have left some gaps in our understanding of the list-decodability of random linear codes. In particular, none of these arguments work well for listrecovery, a generalization of list-decoding that has been useful in a variety of settings. In this work, we present a new approach, which works across parameter regimes and further generalizes to list-recovery. In particular, our argument provides better results for list-decoding and list-recovery over large fields; improved (quasipolynomial) list sizees for high-rate list-recovery of random linear codes; improved algorithmic results for list-decoding; and optimal average-radius list-decoding over constant-sized alphabets.

Original languageEnglish
Title of host publication29th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2018
EditorsArtur Czumaj
PublisherAssociation for Computing Machinery
Pages644-662
Number of pages19
ISBN (Electronic)9781611975031
DOIs
StatePublished - 2018
Event29th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2018 - New Orleans, United States
Duration: Jan 7 2018Jan 10 2018

Publication series

NameProceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms

Conference

Conference29th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2018
Country/TerritoryUnited States
CityNew Orleans
Period01/7/1801/10/18

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