Extracting Mergers and Projections of Partitions

Authors Swastik Kopparty , Vishvajeet N



PDF
Thumbnail PDF

File

LIPIcs.APPROX-RANDOM.2023.52.pdf
  • Filesize: 0.8 MB
  • 22 pages

Document Identifiers

Author Details

Swastik Kopparty
  • Department of Computer Science and Department of Mathematics, University of Toronto, Canada
Vishvajeet N
  • School of Informatics, University of Edinburgh,UK

Cite AsGet BibTex

Swastik Kopparty and Vishvajeet N. Extracting Mergers and Projections of Partitions. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 275, pp. 52:1-52:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2023.52

Abstract

We study the problem of extracting randomness from somewhere-random sources, and related combinatorial phenomena: partition analogues of Shearer’s lemma on projections. A somewhere-random source is a tuple (X_1, …, X_t) of (possibly correlated) {0,1}ⁿ-valued random variables X_i where for some unknown i ∈ [t], X_i is guaranteed to be uniformly distributed. An extracting merger is a seeded device that takes a somewhere-random source as input and outputs nearly uniform random bits. We study the seed-length needed for extracting mergers with constant t and constant error. Since a somewhere-random source has min-entropy at least n, a standard extractor can also serve as an extracting merger. Our goal is to understand whether the further structure of being somewhere-random rather than just having high entropy enables smaller seed-length, and towards this we show: - Just like in the case of standard extractors, seedless extracting mergers with even just one output bit do not exist. - Unlike the case of standard extractors, it is possible to have extracting mergers that output a constant number of bits using only constant seed. Furthermore, a random choice of merger does not work for this purpose! - Nevertheless, just like in the case of standard extractors, an extracting merger which gets most of the entropy out (namely, having Ω(n) output bits) must have Ω(log n) seed. This is the main technical result of our work, and is proved by a second-moment strengthening of the graph-theoretic approach of Radhakrishnan and Ta-Shma to extractors. All this is in contrast to the status for condensing mergers (where the output is only required to have high min-entropy), whose seed-length/output-length tradeoffs can all be fully explained by using standard condensers. Inspired by such considerations, we also formulate a new and basic class of problems in combinatorics: partition analogues of Shearer’s lemma. We show basic results in this direction; in particular, we prove that in any partition of the 3-dimensional cube [0,1]³ into two parts, one of the parts has an axis parallel 2-dimensional projection of area at least 3/4.

Subject Classification

ACM Subject Classification
  • Theory of computation → Expander graphs and randomness extractors
  • Mathematics of computing → Combinatorics
  • Theory of computation → Complexity theory and logic
  • Mathematics of computing → Discrete mathematics
Keywords
  • randomness extractors
  • randomness mergers
  • extracting mergers
  • partitions
  • projections of partitions
  • covers
  • projections of covers

Metrics

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

References

  1. Divesh Aggarwal, Siyao Guo, Maciej Obremski, João Ribeiro, and Noah Stephens-Davidowitz. Extractor Lower Bounds, Revisited. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM), 2020. Google Scholar
  2. Jean Bourgain, Jeff Kahn, Gil Kalai, Yitzhak Katznelson, and Nathan Linial. The Influence of Variables in Product Spaces. In Israel Journal of Mathematics, 1992. Google Scholar
  3. Eshan Chattopadhyay, Jesse Goodman, Vipul Goyal, and Xin Li. Extractors for Adversarial Sources via Extremal Hypergraphs. In Proceedings of the 52nd Annual ACM Symposium on Theory of Computing (STOC), 2020. Google Scholar
  4. Eshan Chattopadhyay and David Zuckerman. Explicit Two-Source Extractors and Resilient Functions. In Annals of Mathematics, 2019. Google Scholar
  5. F.R.K Chung, R.L Graham, P Frankl, and J.B Shearer. Some Intersection Theorems for Ordered Sets and Graphs. Journal of Combinatorial Theory, Series A, 1986. Google Scholar
  6. Zeev Dvir. On The Size of Kakeya Sets in Finite Fields. In Journal of the American Mathematical Society, 2009. Google Scholar
  7. Zeev Dvir, Swastik Kopparty, Shubhangi Saraf, and Madhu Sudan. Extensions to the Method of Multiplicities, with Applications to Kakeya Sets and Mergers. In SIAM Journal on Computing, 2013. Google Scholar
  8. Zeev Dvir and Avi Wigderson. Kakeya Sets, New Mergers and Old Extractors. In Proceedings of the 49th Annual IEEE Symposium on Foundations of Computer Science (FOCS), 2008. Google Scholar
  9. Yuval Filmus, Lianna Hambardzumyan, Hamed Hatami, Pooya Hatami, and David Zuckerman. Biasing Boolean Functions and Collective Coin-Flipping Protocols over Arbitrary Product Distributions. In Proceedings of the 46th International Colloquium on Automata, Languages, and Programming (ICALP), 2019. Google Scholar
  10. Ehud Friedgut. Influences in Product Spaces: KKL and BKKKL Revisited. Comb. Probab. Comput, 2004. Google Scholar
  11. Venkatesan Guruswami, Christopher Umans, and Salil Vadhan. Unbalanced Expanders and Randomness Extractors from Parvaresh-Vardy Codes*. In Journal of the Association for Computing Machinery, 2009. Google Scholar
  12. J. Kahn, G. Kalai, and N. Linial. The influence of variables on Boolean functions. In Proceedings of the 29th Annual IEEE Symposium on Foundations of Computer Science, (FOCS), 1988. Google Scholar
  13. Swastik Kopparty and Vishvajeet N. Extracting mergers and projections of partitions, 2023. URL: https://arxiv.org/abs/2306.16915.
  14. Lynn Harold Loomis and Hassler Whitney. An Inequality Related to the Isoperimetric Inequality. Bull. AMS, 1949. Google Scholar
  15. Chi-Jen Lu, Omer Reingold, Salil Vadhan, and Avi Wigderson. Extractors: Optimal up to Constant Factors. In Proceedings of the 35th Annual ACM Symposium on Theory of Computing (STOC), 2003. Google Scholar
  16. Raghu Meka. Explicit Resilient Functions Matching Ajtai-Linial. In Proceedings of the 2017 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2017. Google Scholar
  17. Jaikumar Radhakrishnan and Amnon Ta-Shma. Bounds for Dispersers, Extractors, and Depth-Two Superconcentrators. In SIAM Journal on Discrete Mathematics, 2000. Google Scholar
  18. Ran Raz. Extractors with Weak Random Seeds. In Proceedings of the 37th Annual ACM Symposium on Theory of Computing (STOC), 2005. Google Scholar
  19. Amnon Ta-Shma. Refining Randomness. In Thesis Submitted to the Hebrew University of Jerusalem, 2000. Google Scholar
  20. Amnon Ta-Shma and Christopher Umans. Better Condensers and New Extractors from Parvaresh-Vardy Codes. In Proceedings of the 27th Conference on Computational Complexity (CCC), 2012. Google Scholar
  21. Salil P. Vadhan. Pseudorandomness. Foundations and Trends in Theoretical Computer Science, 2012. 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