• DocumentCode
    2887051
  • Title

    Strong Convergence of Hybrid Approximate Proximal-Type Algorithm

  • Author

    Li, Wei

  • Author_Institution
    Sch. of Math. & Stat., Hebei Univ. of Econ. & Bus., Shijiazhuang
  • fYear
    2006
  • fDate
    13-16 Aug. 2006
  • Firstpage
    602
  • Lastpage
    606
  • Abstract
    Finding zero points for maximal monotone operator is a very active topic in different branches of mathematical and engineering sciences since many physically significant problems can be ultimately converted to it. Considerable research efforts have been devoted to the study of iterative algorithms of zero points for maximal monotone operators in recent years. By now, there already exist some algorithms, but they are not quite enough to deal with problems defined in a more general space. In this paper, a new hybrid approximate proximal-type algorithm is introduced which is proved to be strongly convergent to zero point of maximal monotone operator in Banach space by using some techniques of Lyapunov functional and generalized projection operator, etc. Moreover, the application of the new algorithm is demonstrated
  • Keywords
    Banach spaces; Lyapunov methods; convergence; iterative methods; mathematical operators; optimisation; Banach space; Lyapunov functional; convergence; generalized projection operator; hybrid approximate proximal-type algorithm; iterative algorithm; maximal monotone operator; Boundary value problems; Convergence; Cybernetics; Hilbert space; Iterative algorithms; Kernel; Machine learning; Machine learning algorithms; Mathematics; Statistics; Topology; Generalized projection operator; Hybrid approximate proximal-type algorithm; Maximal monotone operator 1. Introduction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2006 International Conference on
  • Conference_Location
    Dalian, China
  • Print_ISBN
    1-4244-0061-9
  • Type

    conf

  • DOI
    10.1109/ICMLC.2006.258384
  • Filename
    4028135