List decoding with double samplers

Irit Dinur, Prahladh Harsha, Tali Kaufman, Inbal Livni Navon, Amnon Ta Shma

Research output: Contribution to conferencePaperpeer-review


We develop the notion of double samplers, first introduced by Dinur and Kaufman [DK17], which are samplers with additional combinatorial properties, and whose existence we prove using high dimensional expanders. We show how double samplers give a generic way of amplifying distance in a way that enables efficient list-decoding. There are many error correcting code constructions that achieve large distance by starting with a base code C with moderate distance, and then amplifying the distance using a sampler, e.g., the ABNNR code construction [ABN+92] is such. We show that if the sampler is part of a larger double sampler then the construction has an efficient list-decoding algorithm and the list decoding algorithm is oblivious to the base code C (i.e., it runs the unique decoder for C in a black box way). Our list-decoding algorithm works as follows: it uses a local voting scheme from which it constructs a unique games constraint graph. The constraint graph is an expander, so we can solve unique games efficiently. These solutions are the output of the list decoder. This is a novel use of a unique games algorithm as a subroutine in a decoding procedure, as opposed to the more common situation in which unique games are used for demonstrating hardness results. Double samplers and high dimensional expanders are akin to pseudorandom objects in their utility, but they greatly exceed random objects in their combinatorial properties. We believe that these objects hold significant potential for coding theoretic constructions and view this work as demonstrating the power of double samplers in this context.

Original languageEnglish
Number of pages20
StatePublished - 2019
Event30th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2019 - San Diego, United States
Duration: 6 Jan 20199 Jan 2019


Conference30th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2019
Country/TerritoryUnited States
CitySan Diego

All Science Journal Classification (ASJC) codes

  • Software
  • General Mathematics


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