• DocumentCode
    391363
  • Title

    Discrete optimization, SPSA and Markov chain Monte Carlo methods

  • Author

    Gerencser, L. ; Hill, Stacy D.

  • Author_Institution
    Comput. & Autom. Inst., Hungarian Acad. of Sci., Budapest, Hungary
  • Volume
    2
  • fYear
    2002
  • fDate
    10-13 Dec. 2002
  • Firstpage
    2346
  • Abstract
    The minimization of a convex function defined over the grid Zp is considered. A truncated fixed gain simultaneous perturbation stochastic approximation (SPSA) method is proposed and investigated in combination with devices borrowed from the Markov-chain Monte-Carlo literature. In particular, the performance of the proposed method is improved by choosing suitable acceptance probabilities. A new Markovian optimization problem is formulated to get the best rejection probability and gain. A simulation result is presented.
  • Keywords
    Markov processes; Monte Carlo methods; function approximation; optimisation; probability; Markov-chain; Monte Carlo methods; convergence; convex function; discrete optimization; minimization; rejection probability; simultaneous perturbation stochastic approximation; Automation; Cost function; Grid computing; Optimization methods; Physics computing; Probability distribution; Random variables; Resource management; Stability; Symmetric matrices;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control, 2002, Proceedings of the 41st IEEE Conference on
  • ISSN
    0191-2216
  • Print_ISBN
    0-7803-7516-5
  • Type

    conf

  • DOI
    10.1109/CDC.2002.1184883
  • Filename
    1184883
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=391363