Average performance of Monte Carlo and quasi-Monte Carlo methods for global optimization

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Abstract

Passive algorithms for global optimization of a function choose observation points independently of past observed values. We study the average performance of two common passive algorithms, where the average is with respect to a probability on a function space. We consider the case where the probability is on smooth functions, and compare the results to the case where the probability is on non-differentiable functions. The first algorithm chooses equally spaced observation points, while the second algorithm chooses the observation points independently and uniformly distributed. The average convergence rate is derived for both algorithms.

Original languageEnglish (US)
Pages (from-to)262-265
Number of pages4
JournalWinter Simulation Conference Proceedings
StatePublished - Dec 1 1994
Externally publishedYes
EventProceedings of the 1994 Winter Simulation Conference - Buena Vista, FL, USA
Duration: Dec 11 1994Dec 14 1994

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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