用于选择前m个上下文相关设计的高效学习方法
Efficient Learning for Selecting Top-m Context-Dependent Designs
AI总结：
针对上下文相关决策中确定所有上下文下前m个设计的仿真优化问题，提出贝叶斯框架下的序贯采样策略，推导渐近最优采样比，证明策略一致性与最优性，实验验证其提升了选择效率。
摘要：
我们研究一个面向上下文相关决策的仿真优化问题，目标是确定所有上下文下的前m个设计。在贝叶斯框架下，我们将最优动态采样决策建模为随机动态规划问题，并提出一种序贯采样策略，以高效学习每个设计在各上下文下的性能。我们推导了渐近最优采样比，以实现错误选择概率最坏情况的最优大偏差速率。理论证明该采样策略具有一致性，且其渐近采样比是渐近最优的。数值实验表明，所提方法提升了前m个上下文相关设计选择的效率。
英文摘要：
We consider a simulation optimization problem for a context-dependent decision-making, which aims to determine the top-m designs for all contexts. Under a Bayesian framework, we formulate the optimal dynamic sampling decision as a stochastic dynamic programming problem, and develop a sequential sampling policy to efficiently learn the performance of each design under each context. The asymptotically optimal sampling ratios are derived to attain the optimal large deviations rate of the worst-case of probability of false selection. The proposed sampling policy is proved to be consistent and its asymptotic sampling ratios are asymptotically optimal. Numerical experiments demonstrate that the proposed method improves the efficiency for selection of top-m context-dependent designs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
