Complexity of non-adaptive optimization algorithms for a class of diffusions

James M. Calvin, Peter W. Glynn

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


This paper is concerned with the analysis of the average error in approximating the global minimum of a 1-dimensional, time-homogeneous diffusion by non-adaptive methods. We derive the limiting distribution of the suitably normalized approximation error for both random and deterministic non-adaptive approximation methods. We identify the form of the asymptotically optimal random non-adaptive approximation methods.

Original languageEnglish (US)
Pages (from-to)343-365
Number of pages23
JournalCommunications in Statistics. Part C: Stochastic Models
Issue number3
StatePublished - 1996
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation


  • Average-case complexity
  • Diffusion processes
  • Global optimization


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