Polynomial acceleration of Monte-Carlo global search

Research output: Contribution to journalConference articlepeer-review


In this paper we describe a class of algorithms for approximating the global minimum of a function defined on a subset of d-dimensional Euclidean space. The algorithms are based on adaptively composing a number of simple Monte Carlo searches, and use a memory of a fixed finite number of observations. Within the class of algorithms it is possible to obtain arbitrary polynomial speedup in the asymptotic convergence rate compared with simple Monte Carlo.

Original languageEnglish (US)
Pages (from-to)673-677
Number of pages5
JournalWinter Simulation Conference Proceedings
StatePublished - 1999
Event1999 Winter Simulation Conference Proceedings (WSC) - Phoenix, AZ, USA
Duration: Dec 5 1999Dec 8 1999

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Safety, Risk, Reliability and Quality
  • Chemical Health and Safety
  • Applied Mathematics


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