• DocumentCode
    527727
  • Title

    Monte Carlo integration with small random bits

  • Author

    Zhang, Sheng ; Ye, Peixin ; Long, Jingfan

  • Author_Institution
    Sch. of Math. Sci., Nankai Univ., Tianjin, China
  • Volume
    6
  • fYear
    2010
  • fDate
    10-12 Aug. 2010
  • Firstpage
    2980
  • Lastpage
    2983
  • Abstract
    Motivated by the recent progress on quantum integration, we study restricted Monte Carlo integration for anisotropic Hölder-Nikolskii classes. The results show that with c · log2 n random bits we have the same optimal order for the n-th minimal Monte Carlo integration error as with arbitrary random numbers.
  • Keywords
    Monte Carlo methods; error analysis; integral equations; randomised algorithms; Monte Carlo integration; anisotropic Holder-Nikolskii class; arbitrary random numbers; n-th minimal integration error; random bits; Approximation algorithms; Approximation methods; Complexity theory; Convergence; Monte Carlo methods; Polynomials; Quantum computing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2010 Sixth International Conference on
  • Conference_Location
    Yantai, Shandong
  • Print_ISBN
    978-1-4244-5958-2
  • Type

    conf

  • DOI
    10.1109/ICNC.2010.5583957
  • Filename
    5583957
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=527727