Randomized algorithm for global optimization with bounded memory

Research output: Contribution to journalArticlepeer-review


We describe a class of adaptive algorithms for approximating the global minimum of a function defined on a compact subset of Rd. The algorithms are adaptive versions of Monte Carlo search and use a memory of a fixed number of past observations. By choosing a large enough memory, the convergence rate can be made to exceed any power of the convergence rate obtained with standard Monte Carlo search.

Original languageAmerican English
Pages (from-to)1068-1081
Number of pages14
JournalMathematics and Computers in Simulation
Issue number6
StatePublished - Feb 2010

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science
  • Numerical Analysis
  • Modeling and Simulation
  • Applied Mathematics


  • Global optimization
  • Monte Carlo methods
  • Parallel algorithm
  • Point process
  • Randomized algorithm


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