Comparison of Monte Carlo and deterministic methods for non-adaptive optimization

Research output: Contribution to journalConference article


In this paper we compare the average performance of Monte Carlo methods for global optimization with non-adaptive deterministic alternatives. We analyze the behavior of the algorithms under the assumption of Wiener measure on the space of continuous functions on the unit interval. In this setting we show that the primary strength of the Monte Carlo methods (compositeness) is outweighed by the primary weakness (random gap size) when compared to efficient deterministic methods.

Original languageEnglish (US)
Pages (from-to)348-351
Number of pages4
JournalWinter Simulation Conference Proceedings
StatePublished - Dec 1 1997
Externally publishedYes
EventProceedings of the 1997 Winter Simulation Conference - Atlanta, GA, USA
Duration: Dec 7 1997Dec 10 1997


All Science Journal Classification (ASJC) codes

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

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