通过似然比法进行高效嵌套模拟实验设计

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ben Mingbin Feng, Eunhye Song
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引用次数: 0

摘要

在嵌套模拟文献中,一个常见的假设是实验者可以选择要采样的外部情景的数量。本文考虑的情况是,实验者从外部实体获得一组固定的外部情景。我们提出了一种嵌套模拟实验设计,即通过似然比方法,从一种情景中汇集内部复制来估计另一种情景的条件平均值。在给定外部情景的情况下,我们通过解决一个双级优化问题来决定在每个外部情景下运行多少个内部复制,以及如何汇集内部复制,从而最大限度地减少总的模拟工作量。我们对根据优化实验设计计算出的性能指标估计值的收敛率进行了渐近分析。在某些假设条件下,在仿真预算为 Γ 的情况下,优化设计可使估计值的均方误差达到 O(Γ-1)。数值实验证明,我们的设计优于通过回归汇集复制的最先进设计:由模拟领域编辑 Bruno Tuffin 接受:本研究得到了美国国家科学基金会 [CMMI-2045400] 和加拿大自然科学与工程研究理事会 [RGPIN-2018-03755] 的资助:支持本研究结果的软件可从论文及其补充信息 (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0392) 以及 IJOC GitHub 软件库 (https://github.com/INFORMSJoC/2022.0392) 中获取。完整的 IJOC 软件和数据资源库可从 https://informsjoc.github.io/ 获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Nested Simulation Experiment Design via the Likelihood Ratio Method

In the nested simulation literature, a common assumption is that the experimenter can choose the number of outer scenarios to sample. This paper considers the case when the experimenter is given a fixed set of outer scenarios from an external entity. We propose a nested simulation experiment design that pools inner replications from one scenario to estimate another scenario’s conditional mean via the likelihood ratio method. Given the outer scenarios, we decide how many inner replications to run at each outer scenario as well as how to pool the inner replications by solving a bilevel optimization problem that minimizes the total simulation effort. We provide asymptotic analyses on the convergence rates of the performance measure estimators computed from the optimized experiment design. Under some assumptions, the optimized design achieves O(Γ1) mean squared error of the estimators given simulation budget Γ. Numerical experiments demonstrate that our design outperforms a state-of-the-art design that pools replications via regression.

History: Accepted by Bruno Tuffin, Area Editor for Simulation.

Funding: This work was supported by the National Science Foundation [Grant CMMI-2045400] and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2018-03755].

Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0392) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0392). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/.

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来源期刊
Informs Journal on Computing
Informs Journal on Computing 工程技术-计算机:跨学科应用
CiteScore
4.20
自引率
14.30%
发文量
162
审稿时长
7.5 months
期刊介绍: The INFORMS Journal on Computing (JOC) is a quarterly that publishes papers in the intersection of operations research (OR) and computer science (CS). Most papers contain original research, but we also welcome special papers in a variety of forms, including Feature Articles on timely topics, Expository Reviews making a comprehensive survey and evaluation of a subject area, and State-of-the-Art Reviews that collect and integrate recent streams of research.
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