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Complete expected improvement converges to an optimal budget allocation

Published online by Cambridge University Press:  22 July 2019

Ye Chen
Affiliation:
Virginia Common wealth University
Ilya O. Ryzhov
Affiliation:
University of Maryland
Corresponding
E-mail address:

Abstract

The ranking and selection problem is a well-known mathematical framework for the formal study of optimal information collection. Expected improvement (EI) is a leading algorithmic approach to this problem; the practical benefits of EI have repeatedly been demonstrated in the literature, especially in the widely studied setting of Gaussian sampling distributions. However, it was recently proved that some of the most well-known EI-type methods achieve suboptimal convergence rates. We investigate a recently proposed variant of EI (known as ‘complete EI’) and prove that, with some minor modifications, it can be made to converge to the rate-optimal static budget allocation without requiring any tuning.

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Original Article
Copyright
© Applied Probability Trust 2019 

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