Efficient Simulation of Large-Scale Systems
Efficient Simulation of Large-Scale Systems
批准号:
9900117
负责人:
James Calvin
金额:
$18.94万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
1999
资助国家:
美国
项目状态:
已结题
起止时间:
1999-06-15 至 2004-05-31
中文摘要
这项拨款为开发新方法提供资金,以提高大规模随机系统的模拟效率,例如在制造业、电信、生产/库存、金融和运输中出现的系统。许多奖励技术背后的基本思想是从模拟中提取或利用比标准方法更多的信息。这些方法与现有的方差减少技术(如控制变量)相关,控制变量收集用于修改估计器的附加数据,从而减少可变性。主要研究人员建议开发其他方法,类似地利用模拟中被忽略的信息来改进估计器。提出的几种技术采用仿真输出的特定实现,并从中构建许多其他可能的实现。根据每个实现计算感兴趣的性能度量的估计值,然后对估计值求平均值。这导致了比标准方法更低的可变性。主要研究人员计划在各种假设下建立这些方法的有效性,并开发这些想法的计算效率实现。期望的结果之一是表明所提出的方法在某些条件下是最优的。如果成功,该项目的结果将导致大规模系统模拟效率的显著提高。这些方法的基本思想是非常通用和通用的,它们可以与其他现有的仿真技术相结合。
英文摘要
This grant provides funding for the development of new methodologies for improving the efficiency of simulations of large-scale stochastic systems, such as those arising in manufacturing, telecommunications, production/inventory, finance, and transportation. The basic ideas underlying many of the award techniques is to extract or utilize more information from a simulation than is done in the standard approaches.The methods are related to existing variance-reduction techniques such as control variates, which collect additional data that are used to modify the estimator, leading to less variability. The Principal Investigators propose to develop other approaches that similarly exploit ignored information from simulations to improve the estimator. Several of the proposed techniques take a particular realization of the simulation output and construct many other possible realizations from it. Estimates of the performance measure of interest are computed from each realization, and the estimates are then averaged. This leads to lower variability thanthe standard approach. The Principal Investigators plan to establish the validity of these methodologies under various assumptions and develop computationally efficient implementations of the ideas. One of the desired outcomes is to show that the proposed methods are optimal under certain conditions. If successful, the results of the project will lead to significant improvements in the efficiency of simulations of large-scale systems. The basic ideas underlying the methods are quite general and versatile, and they can be combined with other existing simulation techniques.
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专著(0)
科研奖励(0)
会议论文
Optimization Algorithms for Decision Problems with Many Variables
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批准号:1562466
-
项目类别:Standard Grant
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资助金额:$27.88万
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财政年份:2016
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负责人:James Calvin
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依托单位:
Algorithms and Complexity for Global Optimization
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批准号:0825381
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项目类别:Standard Grant
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资助金额:$26.0万
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财政年份:2008
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负责人:James Calvin
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依托单位:
MRI: Development of a High Density, High Performance Beowulf Cluster
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批准号:0216275
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项目类别:Standard Grant
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资助金额:$40.52万
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财政年份:2002
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负责人:James Calvin
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依托单位:
Average Complexity of Global Optimization
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批准号:9696243
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项目类别:Standard Grant
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资助金额:$10.27万
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财政年份:1996
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负责人:James Calvin
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依托单位:
Average Complexity of Global Optimization
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批准号:9500173
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项目类别:Standard Grant
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资助金额:$12.6万
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财政年份:1995
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负责人:James Calvin
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依托单位:
Research Initiation: Stochastic Optimization and Search Algorithms
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批准号:9010770
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项目类别:Standard Grant
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资助金额:$5.0万
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财政年份:1990
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负责人:James Calvin
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依托单位:
国内基金
海外基金
Simulation and certification of the ground state of many-body systems on quantum simulators
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批准号:--
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项目类别:--
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资助金额:40万元
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批准年份:2020
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负责人:Abolfazl Bayat
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依托单位: