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Nonadaptive Univariate Optimization for Observations with Noise

  • James M. Calvin
Part of the Optimization and Its Applications book series (SOIA, volume 4)

Abstract

It is much more difficult to approximate the minimum of a function using noise-corrupted function evaluations than when the function can be evaluated precisely. This chapter is concerned with the question of exactly how much harder it is in a particular setting; namely, on average when the objective function is a Wiener process, the noise is independent Gaussian, and nonadaptive algorithms are considered.

Keywords

Global Optimization Conditional Distribution Wiener Process Uniform Grid Regular Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • James M. Calvin
    • 1
  1. 1.New Jersey Institute of TechnologyNewarkUSA

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