Online Supplement to ‘Myopic Allocation Policy with Asymptotically Optimal Sampling Rate’
Online Supplement to ‘Myopic Allocation Policy with Asymptotically Optimal Sampling Rate’
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Date
2016
Authors
Peng, Yijie
Fu, Michael
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Abstract
In this online appendix, we test the performance of the AOMAP (asymptotically optimal myopic allocation policy) algorithm under the unknown variances scenario and compare it with EI (expected improvement) and OCBA (optimal computing budget allocation).
Notes
This document is the Online Supplement to ‘Myopic Allocation Policy with Asymptotically Optimal Sampling Rate,’ to be published in the IEEE Transactions of Automatic Control in 2017.