Efficient Sampling Policy for Selecting a Good Enough Subset
Optimization and Control
2023-05-09 v1
Abstract
The note studies the problem of selecting a good enough subset out of a finite number of alternatives under a fixed simulation budget. Our work aims to maximize the posterior probability of correctly selecting a good subset. We formulate the dynamic sampling decision as a stochastic control problem in a Bayesian setting. In an approximate dynamic programming paradigm, we propose a sequential sampling policy based on value function approximation. We analyze the asymptotic property of the proposed sampling policy. Numerical experiments demonstrate the efficiency of the proposed procedure.
Cite
@article{arxiv.2111.14534,
title = {Efficient Sampling Policy for Selecting a Good Enough Subset},
author = {Gongbo Zhang and Bin Chen and Qing-shan Jia and Yijie Peng},
journal= {arXiv preprint arXiv:2111.14534},
year = {2023}
}