Plausible Screening Using Functional Properties for Simulations with Large Solution Spaces
针对仿真模型产生大量解空间的问题，提出利用函数性质（如凸性）筛选不可行解，通过优化问题衡量有限仿真实验与解可接受性的一致性，并提供统计置信保证。
Simulation Solution Screening Using Functional Properties Simulation models today give rise to problems with large numbers of simulated scenarios or solutions—more than can be simulated exhaustively. Fortunately, users of these models may be able to verify or infer properties, such as convexity, of a performance measure of interest when viewed as a function over the space of solutions. In “Plausible Screening Using Functional Properties for Simulations with Large Solution Spaces,” Eckman, Plumlee and Nelson introduce a framework in which such properties are exploited to avoid simulating solutions with unacceptable performances. Their methods solve optimization problems that measure how well the result of a limited simulation experiment agrees with the claim that a solution is acceptable. These methods deliver desirable statistical guarantees of confidence and consistency. Numerical experiments illustrate how functional properties coupled with small simulation experiments can avoid many simulations for simulation-optimization problems.