An adaptive univariate global optimization algorithm and its convergence rate under the Wiener measure

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Abstract

We describe an adaptive algorithm for approximating the global minimum of a continuous univariate function. The convergence rate of the error is studied for the case of a random objective function distributed according to the Wiener measure.

Original languageEnglish (US)
Pages (from-to)471-488
Number of pages18
JournalInformatica
Volume22
Issue number4
StatePublished - Dec 1 2011

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Applied Mathematics

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