.png){kind=icon}
.png){kind=icon}
Return
Efficient Learning for Selecting Top- m Context-Dependent Designs
.png){kind=icon}
OA
AI
DOI：10.1109/TASE.2024.3391020.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We consider a simulation optimization problem for 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 probability of false selection. The proposed sampling policy is proved to be consistent, and its asymptotic sampling ratios are shown to be asymptotically optimal. Numerical experiments demonstrate that the proposed method improves the efficiency for selecting top- m context-dependent designs.
Keywords:
Simulation optimization context-dependent decision
top-m selection
dynamic sampling
asymptotic optimality
Journal
IF:
6.4
Papers:
4.9K
Citations:
1.6W