skip to main content
10.1145/1536414.1536502acmconferencesArticle/Chapter ViewAbstractPublication PagesstocConference Proceedingsconference-collections
research-article

Explicit construction of a small epsilon-net for linear threshold functions

Published:31 May 2009Publication History

ABSTRACT

We give explicit constructions of epsilon nets for linear threshold functions on the binary cube and on the unit sphere. The size of the constructed nets is polynomial in the dimension n and in 1/ε. To the best of our knowledge no such constructions were previously known. Our results match, up to the exponent of the polynomial, the bounds that are achieved by probabilistic arguments. As a corollary we also construct subsets of the binary cube that have size polynomial in n and covering radius of n/2 - c√{n log n}, for any constant c. This improves upon the well known construction of dual BCH codes that only guarantee covering radius of n/2 - c√n.

References

  1. and P.Sarnak A. Lubotzky, R. Phillips. Ramanujan graphs. Combinatorica, 8(3):261--277, 1988.Google ScholarGoogle ScholarCross RefCross Ref
  2. N. Alon. Private communication, 2008.Google ScholarGoogle Scholar
  3. N. Alon, U. Feige, A. Wigderson, and D. Zuckerman. Derandomized graph products. Computational Complexity, 5(1):60--75, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. N. Alon, G. Gutin, and M. Krivelevich. Algorithms with large domination ratio. J. Algorithms, 50(1):118--131, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. N. Alon, H. Kaplan, G. Nivasch, M. Sharir, and S. Smorodinsky. Weak ε-nets and interval chains. In Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms (SODA), pages 1194--1203, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. N. Alon and J. Spencer. the probabilistic method. J. Wiley, 3 edition, 2008.Google ScholarGoogle Scholar
  7. B. Berger. The fourth moment method. SIAM J. Comput., 26(4):1188--1207, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. E. J. Candés and T. Tao. Near-optimal signal recovery from random projections: universal encoding strategies. IEEE Trans. Inform. Theory, 52(12):5406--5425, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. L. Carter and M. N. Wegman. Universal classes of hash functions. J. Comput. Syst. Sci., 18:143--154, 1979.Google ScholarGoogle ScholarCross RefCross Ref
  10. B. Chazelle. Computational geometry: a retrospective. In Proceedings of the twenty-sixth annual ACM symposium on Theory of computing (STOC), pages 75--94, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. D. L. Donoho. Compressed sensing. IEEE Transactions on Information Theory, 52(4):1289--1306, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. L. Engebretsen, P. Indyk, and R. O’Donnell. Derandomized dimensionality reduction with applications. In Proceedings of the Thirteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 705--712, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. G. Even, O. Goldreich, M. Luby, N. Nisan, and B. Velickovic. Approximations of general independent distributions. In Proceedings of the 24th Annual ACM Symposium on Theory of Computing, pages 10--16, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. M. L. Fredman, J. Komlós, and E. Szemerédi. Storing a sparse table with 0(1) worst case access time. J. ACM, 31(3):538--544, 1984. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. V. Guruswami, J. R. Lee, and A. A. Razborov. Almost euclidean subspaces of ln1 via expander codes. In Proceedings of the Nineteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 353--362, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. V. Guruswami, J. R. Lee, and A. Wigderson. Euclidean sections of with sublinear randomness and error-correction over the reals. In Approximation, Randomization and Combinatorial Optimization. Algorithms and Techniques, 11th International Workshop, APPROX 2008, and 12th International Workshop, RANDOM 2008, pages 444--454, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. P. Indyk. Uncertainty principles, extractors, and explicit embeddings of l2 into l1. In Proceedings of the 39th Annual ACM Symposium on Theory of Computing (STOC), pages 615--620, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. W. B. Johnson and J. Lindenstrauss. Extensions of lipschitz maps into a hilbert space. Contemporary Mathematics, 26:189--206, 1984.Google ScholarGoogle ScholarCross RefCross Ref
  19. N. Linial, M. Luby, M. E. Saks, and D. Zuckerman. Efficient construction of a small hitting set for combinatorial rectangles in high dimension. Combinatorica, 17(2):215--234, 1997.Google ScholarGoogle ScholarCross RefCross Ref
  20. G. A. Margulis. Explicit group-theoretic constructions of combinatorial schemes and their applications in the construction of expanders and concentrators. Problems of Information Transmission, 24(1):39--46, 1988.Google ScholarGoogle Scholar
  21. J. Matousek. Lectures on discrete Geometry. GTM. springer, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. J. Naor and M. Naor. Small-bias probability spaces: Efficient constructions and applications. SIAM J. on Computing, 22(4):838--856, 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. J. P. Schmidt and A. Siegel. The analysis of closed hashing under limited randomness (extended abstract). In Proceedings of the Twenty Second Annual ACM Symposium on Theory of Computing (STOC), pages 224--234, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. D. Sivakumar. Algorithmic derandomization via complexity theory. In Proceedings on 34th Annual ACM Symposium on Theory of Computing (STOC), pages 619--626, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. E. Viola. The sum of d small-bias generators fools polynomials of degree d. In Proceedings of the 23rd Annual IEEE Conference on Computational Complexity (CCC), pages 124--127, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Explicit construction of a small epsilon-net for linear threshold functions

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      STOC '09: Proceedings of the forty-first annual ACM symposium on Theory of computing
      May 2009
      750 pages
      ISBN:9781605585062
      DOI:10.1145/1536414

      Copyright © 2009 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 31 May 2009

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate1,469of4,586submissions,32%

      Upcoming Conference

      STOC '24
      56th Annual ACM Symposium on Theory of Computing (STOC 2024)
      June 24 - 28, 2024
      Vancouver , BC , Canada

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader