• DocumentCode
    3119350
  • Title

    Compressive binary search

  • Author

    Davenport, Mark A. ; Arias-Castro, Ery

  • Author_Institution
    Dept. of Stat., Stanford Univ., Stanford, CA, USA
  • fYear
    2012
  • fDate
    1-6 July 2012
  • Firstpage
    1827
  • Lastpage
    1831
  • Abstract
    In this paper we consider the problem of locating a nonzero entry in a high-dimensional vector from possibly adaptive linear measurements. We consider a recursive bisection method which we dub the compressive binary search and show that it improves on what any nonadaptive method can achieve. We also establish a non-asymptotic lower bound that applies to all methods, regardless of their computational complexity. Combined, these results show that the compressive binary search is within a double logarithmic factor of the optimal performance.
  • Keywords
    computational complexity; recursive functions; vectors; adaptive linear measurement; compressive binary search; computational complexity; double logarithmic factor; high-dimensional vector; nonadaptive method; nonasymptotic lower bound; recursive bisection method; Algorithm design and analysis; Context; Matching pursuit algorithms; Noise measurement; Sensors; Testing; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory Proceedings (ISIT), 2012 IEEE International Symposium on
  • Conference_Location
    Cambridge, MA
  • ISSN
    2157-8095
  • Print_ISBN
    978-1-4673-2580-6
  • Electronic_ISBN
    2157-8095
  • Type

    conf

  • DOI
    10.1109/ISIT.2012.6283595
  • Filename
    6283595