Average performance of passive algorithms 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 under the assumption of Brownian motion prior. The first algorithm chooses equally spaced observation points, while the second algorithm chooses the observation points independently and uniformly distributed. The average convergence rate for both is O(n-1/2), with the second algorithm approximately 82% as efficient as the first.

Original languageEnglish (US)
Pages (from-to)608-617
Number of pages10
JournalJournal of Mathematical Analysis and Applications
Volume191
Issue number3
DOIs
StatePublished - May 1 1995
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Analysis
  • Applied Mathematics

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