• Andrey Pepelyshev
  • Anatoly Zhigljavsky
  • Antanas Žilinskas

We investigate the rate of convergence of general global random search (GRS) algorithms. We show that if the dimension of the feasible domain is large then it is impossible to give any guarantee that the global minimizer is found by a general GRS algorithm with reasonable accuracy. We then study precision of statistical estimates of the global minimum in the case of large dimensions. We show that these estimates also suffer the curse of dimensionality. Finally, we demonstrate that the use of quasi-random points in place of the random ones does not give any visible advantage in large dimensions.

Original languageEnglish
Pages (from-to)57-71
Number of pages15
JournalJournal of Global Optimization
Volume71
Issue number1
DOIs
StatePublished - 1 May 2018

    Research areas

  • Extreme value statistics, Global optimization, Random search, Statistical models

    Scopus subject areas

  • Computer Science Applications
  • Control and Optimization
  • Management Science and Operations Research
  • Applied Mathematics

ID: 50725798