Nonadaptive Univariate Optimization for Observations with Noise
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.
KeywordsGlobal Optimization Conditional Distribution Wiener Process Uniform Grid Regular Sequence
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