Tight Bounds on List-Decodable and List-Recoverable Zero-Rate Codes

Authors Nicolas Resch , Chen Yuan , Yihan Zhang



PDF
Thumbnail PDF

File

LIPIcs.ITCS.2025.82.pdf
  • Filesize: 0.85 MB
  • 21 pages

Document Identifiers

Author Details

Nicolas Resch
  • Informatics' Institute, University of Amsterdam, The Netherlands
Chen Yuan
  • School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China
Yihan Zhang
  • Institute of Science and Technology Austria, Klosterneuburg, Austria

Cite As Get BibTex

Nicolas Resch, Chen Yuan, and Yihan Zhang. Tight Bounds on List-Decodable and List-Recoverable Zero-Rate Codes. In 16th Innovations in Theoretical Computer Science Conference (ITCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 325, pp. 82:1-82:21, Schloss Dagstuhl – Leibniz-Zentrum fΓΌr Informatik (2025) https://doi.org/10.4230/LIPIcs.ITCS.2025.82

Abstract

In this work, we consider the list-decodability and list-recoverability of codes in the zero-rate regime. Briefly, a code π’ž βŠ† [q]ⁿ is (p,𝓁,L)-list-recoverable if for all tuples of input lists (Y₁,… ,Y_n) with each Y_i βŠ† [q] and |Y_i| = 𝓁, the number of codewords c ∈ π’ž such that c_i βˆ‰ Y_i for at most pn choices of i ∈ [n] is less than L; list-decoding is the special case of 𝓁 = 1. In recent work by Resch, Yuan and Zhang (ICALP 2023) the zero-rate threshold for list-recovery was determined for all parameters: that is, the work explicitly computes p_*: = p_*(q,𝓁,L) with the property that for all Ξ΅ > 0 (a) there exist positive-rate (p_*-Ξ΅,𝓁,L)-list-recoverable codes, and (b) any (p_*+Ξ΅,𝓁,L)-list-recoverable code has rate 0. In fact, in the latter case the code has constant size, independent on n. However, the constant size in their work is quite large in 1/Ξ΅, at least |π’ž| β‰₯ (1/(Ξ΅))^O(q^L).
Our contribution in this work is to show that for all choices of q,𝓁 and L with q β‰₯ 3, any (p_*+Ξ΅,𝓁,L)-list-recoverable code must have size O_{q,𝓁,L}(1/Ξ΅), and furthermore this upper bound is complemented by a matching lower bound Ξ©_{q,𝓁,L}(1/Ξ΅). This greatly generalizes work by Alon, Bukh and Polyanskiy (IEEE Trans. Inf. Theory 2018) which focused only on the case of binary alphabet (and thus necessarily only list-decoding). We remark that we can in fact recover the same result for q = 2 and even L, as obtained by Alon, Bukh and Polyanskiy: we thus strictly generalize their work. 
Our main technical contribution is to (a) properly define a linear programming relaxation of the list-recovery condition over large alphabets; and (b) to demonstrate that a certain function defined on a q-ary probability simplex is maximized by the uniform distribution. This represents the core challenge in generalizing to larger q (as a binary simplex can be naturally identified with a one-dimensional interval). We can subsequently re-utilize certain Schur convexity and convexity properties established for a related function by Resch, Yuan and Zhang along with ideas of Alon, Bukh and Polyanskiy.

Subject Classification

ACM Subject Classification
  • Mathematics of computing β†’ Coding theory
Keywords
  • List Decoding
  • List Recovery
  • Zero Rate

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Noga Alon, Boris Bukh, and Yury Polyanskiy. List-decodable zero-rate codes. IEEE Transactions on Information Theory, 65(3):1657-1667, 2018. URL: https://doi.org/10.1109/TIT.2018.2868957.
  2. LΓ‘szlΓ³ Babai, Lance Fortnow, Noam Nisan, and Avi Wigderson. BPP has subexponential time simulations unless EXPTIME has publishable proofs. Comput. Complex., 3:307-318, 1993. URL: https://doi.org/10.1007/BF01275486.
  3. L. A. Bassalygo. New upper bounds for error-correcting codes. Probl. of Info. Transm., 1:32-35, 1965. Google Scholar
  4. Vladimir M Blinovsky. Bounds for codes in the case of list decoding of finite volume. Problems of Information Transmission, 22:7-19, 1986. Google Scholar
  5. Vladimir M Blinovsky. Code bounds for multiple packings over a nonbinary finite alphabet. Problems of Information Transmission, 41:23-32, 2005. URL: https://doi.org/10.1007/S11122-005-0007-5.
  6. Vladimir M Blinovsky. On the convexity of one coding-theory function. Problems of Information Transmission, 44:34-39, 2008. URL: https://doi.org/10.1134/S0032946008010031.
  7. Philippe Delsarte. An algebraic approach to the association schemes of coding theory. Philips Res. Rep. Suppl., 10:vi+-97, 1973. Google Scholar
  8. Dean Doron, Dana Moshkovitz, Justin Oh, and David Zuckerman. Nearly optimal pseudorandomness from hardness. In 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS), pages 1057-1068. IEEE, 2020. URL: https://doi.org/10.1109/FOCS46700.2020.00102.
  9. Dean Doron and Mary Wootters. High-probability list-recovery, and applications to heavy hitters. In 49th International Colloquium on Automata, Languages, and Programming (ICALP 2022). Schloss Dagstuhl - Leibniz-Zentrum fΓΌr Informatik, 2022. URL: https://doi.org/10.4230/LIPICS.ICALP.2022.55.
  10. Peter Elias. List decoding for noisy channels. Wescon Convention Record, Part 2, pages 94-104, 1957. Google Scholar
  11. Peter Elias. Error-correcting codes for list decoding. IEEE Transactions on Information Theory, 37(1):5-12, 1991. URL: https://doi.org/10.1109/18.61123.
  12. Edgar N Gilbert. A comparison of signalling alphabets. The Bell System Technical Journal, 31(3):504-522, 1952. Google Scholar
  13. Oded Goldreich and Leonid A Levin. A hard-core predicate for all one-way functions. In Proceedings of the 21st Annual ACM Symposium on Theory of Computing (STOC), pages 25-32. ACM, 1989. URL: https://doi.org/10.1145/73007.73010.
  14. Venkatesan Guruswami, Christopher Umans, and Salil Vadhan. Unbalanced expanders and randomness extractors from parvaresh-vardy codes. Journal of the ACM (JACM), 56(4):1-34, 2009. URL: https://doi.org/10.1145/1538902.1538904.
  15. Iftach Haitner, Yuval Ishai, Eran Omri, and Ronen Shaltiel. Parallel hashing via list recoverability. In Annual Cryptology Conference, pages 173-190. Springer, 2015. URL: https://doi.org/10.1007/978-3-662-48000-7_9.
  16. Justin Holmgren, Alex Lombardi, and Ron D Rothblum. Fiat-shamir via list-recoverable codes (or: parallel repetition of gmw is not zero-knowledge). In Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, pages 750-760, 2021. URL: https://doi.org/10.1145/3406325.3451116.
  17. Piotr Indyk, Hung Q Ngo, and Atri Rudra. Efficiently decodable non-adaptive group testing. In Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms, pages 1126-1142. SIAM, 2010. URL: https://doi.org/10.1137/1.9781611973075.91.
  18. Jeffrey C Jackson. An efficient membership-query algorithm for learning DNF with respect to the uniform distribution. Journal of Computer and System Sciences, 55(3):414-440, 1997. URL: https://doi.org/10.1006/JCSS.1997.1533.
  19. Eyal Kushilevitz and Yishay Mansour. Learning decision trees using the Fourier spectrum. SIAM Journal on Computing, 22(6):1331-1348, 1993. URL: https://doi.org/10.1137/0222080.
  20. VI Levenshtein. Application of hadamard matrices on coding problem. Problems of Cybernetica, 5:123-136, 1961. Google Scholar
  21. Richard J Lipton. Efficient checking of computations. In Proceedings of the 7th Annual Symposium on Theoretical Aspects of Computer Science (STACS), pages 207-215. Springer, 1990. URL: https://doi.org/10.1007/3-540-52282-4_44.
  22. Robert J. McEliece, Eugene R. Rodemich, Howard Rumsey, Jr., and Lloyd R. Welch. New upper bounds on the rate of a code via the Delsarte-MacWilliams inequalities. IEEE Trans. Inform. Theory, IT-23(2):157-166, 1977. URL: https://doi.org/10.1109/tit.1977.1055688.
  23. Hung Q Ngo, Ely Porat, and Atri Rudra. Efficiently decodable error-correcting list disjunct matrices and applications. In International Colloquium on Automata, Languages, and Programming, pages 557-568. Springer, 2011. Google Scholar
  24. Nicolas Resch. List-decodable codes:(randomized) constructions and applications. School Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA, Tech. Rep., CMU-CS-20-113, 2020. Google Scholar
  25. Nicolas Resch, Chen Yuan, and Yihan Zhang. Zero-rate thresholds and new capacity bounds for list-decoding and list-recovery. arXiv preprint, 2022. URL: https://doi.org/10.48550/arXiv.2210.07754.
  26. Madhu Sudan, Luca Trevisan, and Salil Vadhan. Pseudorandom generators without the XOR lemma. Journal of Computer and System Sciences, 62(2):236-266, 2001. URL: https://doi.org/10.1006/JCSS.2000.1730.
  27. Michael A Tsfasman, SG Vlădutx, and Th Zink. Modular curves, shimura curves, and goppa codes, better than varshamov-gilbert bound. Mathematische Nachrichten, 109(1):21-28, 1982. Google Scholar
  28. RR Varshamov. Estimate of the number of signals in error correcting codes. Docklady Akad. Nauk, SSSR, 117:739-741, 1957. Google Scholar
  29. Lloyd R. Welch, Robert J. McEliece, and Howard Rumsey, Jr. A low-rate improvement on the Elias bound. IEEE Trans. Inform. Theory, IT-20:676-678, 1974. URL: https://doi.org/10.1109/tit.1974.1055279.
  30. Jack Wozencraft. List decoding. Quarter Progress Report, 48:90-95, 1958. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail