Parametric Scenario Optimization under Limited Data: A Distributionally Robust Optimization View

IF 0.7 4区计算机科学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

ACM Transactions on Modeling and Computer Simulation Pub Date : 2020-11-23 DOI:10.1145/3410152

LamHenry, LiFengpei

引用次数: 0

Abstract

We consider optimization problems with uncertain constraints that need to be satisfied probabilistically. When data are available, a common method to obtain feasible solutions for such problems is ...

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有限数据下的参数化场景优化:一种分布鲁棒优化观点

我们考虑具有不确定约束的优化问题，这些问题需要在概率上得到满足。当数据可用时，获得此类问题可行解决方案的常用方法是……

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来源期刊

ACM Transactions on Modeling and Computer Simulation 工程技术-计算机：跨学科应用

CiteScore

2.50

自引率

22.20%

发文量

审稿时长

>12 weeks

期刊介绍： The ACM Transactions on Modeling and Computer Simulation (TOMACS) provides a single archival source for the publication of high-quality research and developmental results referring to all phases of the modeling and simulation life cycle. The subjects of emphasis are discrete event simulation, combined discrete and continuous simulation, as well as Monte Carlo methods. The use of simulation techniques is pervasive, extending to virtually all the sciences. TOMACS serves to enhance the understanding, improve the practice, and increase the utilization of computer simulation. Submissions should contribute to the realization of these objectives, and papers treating applications should stress their contributions vis-á-vis these objectives.