AI 中文总结
针对有限模拟系统的排序与选择问题，提出基于置信区域的通用筛选框架，其子类程序时间复杂度低、支持并行化，经数值实验验证有效高效。
AI 中文摘要
我们提出了一种通用框架，用于设计针对具有有限个模拟系统的问题的筛选程序，这类问题也称为排序与选择问题。该框架为筛选提供了一种新颖视角：保留或剔除系统的决策基于未知问题实例的置信区域，而非对估计性能的比较。具体而言，若某系统在置信区域内包含的响应向量的某些合理配置下具有可接受性能，则保留该系统。这种视角有助于设计出能以高概率保证返回所有可接受系统或每个可接受系统的程序，且可适配多种已深入研究的可接受性定义，包括关于随机约束的可行性及针对一个或多个目标的最优性。我们进一步研究了该框架的一个子类，其能生成简单且计算高效的筛选程序，这类程序通常具有比现有方法更低阶的时间复杂度，且天然支持并行化而不损失筛选能力。我们通过数值实验验证了这些程序的有效性与效率。
英文摘要
We introduce a general-purpose framework for designing screening procedures for problems featuring a finite set of simulated systems, a.k.a. ranking-and-selection problems. The framework offers a novel perspective on screening in which decisions to retain or eliminate systems are based on confidence regions for the unknown problem instance rather than comparisons of estimated performances. Specifically, a system is retained if it has acceptable performance under some plausible configuration of response vectors contained in the confidence region. This perspective facilitates the design of procedures that guarantee to return either all acceptable systems, or each acceptable system, with high probability and accommodates many well-studied definitions of acceptability, including feasibility with respect to stochastic constraints and optimality with respect to one or more objectives. We further study a subclass of the framework that yields simple and computationally efficient screening procedures that often have lower-order time complexity than existing methods and naturally supports parallelization without loss of screening power. We demonstrate the effectiveness and efficiency of the procedures through numerical experiments.
