Nonadaptive Univariate Optimization for Observations with Noise

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


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.


Global Optimization Conditional Distribution Wiener Process Uniform Grid Regular Sequence 
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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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