Optimally resilient codes for list-decoding from insertions and deletions

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Abstract

We give a complete answer to the following basic question: "What is the maximal fraction of deletions or insertions tolerable by q-ary list-decodable codes with non-vanishing information rate?" This question has been open even for binary codes, including the restriction to the binary insertion-only setting, where the best-known result was that a 3≤ 0.707 fraction of insertions is tolerable by some binary code family. For any desired ">0, we construct a family of binary codes of positive rate which can be efficiently list-decoded from any combination of γfraction of insertions and δfraction of deletions as long as 3+2≤ 1-ϵ. On the other hand, for any 3, δwith 3+2=1 list-decoding is impossible. Our result thus precisely characterizes the feasibility region of binary list-decodable codes for insertions and deletions. We further generalize our result to codes over any finite alphabet of size q. Surprisingly, our work reveals that the feasibility region for q>2 is not the natural generalization of the binary bound above. We provide tight upper and lower bounds that precisely pin down the feasibility region, which turns out to have a (q-1)-piece-wise linear boundary whose q corner-points lie on a quadratic curve. The main technical work in our results is proving the existence of code families of sufficiently large size with good list-decoding properties for any combination of ,γwithin the claimed feasibility region. We achieve this via an intricate analysis of codes introduced by [Bukh, Ma; SIAM J. Discrete Math; 2014]. Finally, we give a simple yet powerful concatenation scheme for list-decodable insertion-deletion codes which transforms any such (non-efficient) code family (with information rate zero) into an efficiently decodable code family with constant rate.

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Guruswami, V., Haeupler, B., & Shahrasbi, A. (2020). Optimally resilient codes for list-decoding from insertions and deletions. In Proceedings of the Annual ACM Symposium on Theory of Computing (pp. 524–537). Association for Computing Machinery. https://doi.org/10.1145/3357713.3384262

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