论文标题
有效学习，用于聚类和优化上下文依赖性设计
Efficient Learning for Clustering and Optimizing Context-Dependent Designs
论文作者
论文摘要
我们考虑了与上下文有关的决策制定的仿真优化问题。提出了高斯混合模型，以捕获与上下文相关设计的性能聚类现象。在贝叶斯框架下，我们制定了动态抽样策略，以有效地了解每个设计的每个群集的全局信息，也可以在所有情况下选择最佳设计。事实证明，提出的采样策略是一致的，并实现了渐近的最佳抽样比。数值实验表明，提出的采样策略可显着提高上下文依赖性模拟优化的效率。
We consider a simulation optimization problem for a context-dependent decision-making. A Gaussian mixture model is proposed to capture the performance clustering phenomena of context-dependent designs. Under a Bayesian framework, we develop a dynamic sampling policy to efficiently learn both the global information of each cluster and local information of each design for selecting the best designs in all contexts. The proposed sampling policy is proved to be consistent and achieve the asymptotically optimal sampling ratio. Numerical experiments show that the proposed sampling policy significantly improves the efficiency in context-dependent simulation optimization.
