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Statements

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dbr:List_of_numerical_analysis_topics
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dbr:Optimal_computing_budget_allocation
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最优计算量分配 Optimal computing budget allocation
rdfs:comment
最优计算量分配（OCBA） 是最早由陈俊宏教授于90年代中期提出的一个概念。这一方法试图在找到一个最优决策的前提下最大化仿真效率。 简言之，OCBA是一种仿真方法，它能够在给定一组仿真参数的情况下，帮助确定所需的仿真次数及（或）所需的仿真时间，以达到可接受的（或最好的）结果。 其具体做法是通过使用一个渐进框架对最优分配的结构进行分析。 In computer science, optimal computing budget allocation (OCBA) is an approach to maximize the overall simulation efficiency for finding an optimal decision. It was introduced in the mid-1990s by Dr. Chun-Hung Chen. OCBA determines the number of replications or the simulation time that is needed in order to receive acceptable or best results within a set of given parameters. This is accomplished by using an asymptotic framework to analyze the structure of the optimal allocation.
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In computer science, optimal computing budget allocation (OCBA) is an approach to maximize the overall simulation efficiency for finding an optimal decision. It was introduced in the mid-1990s by Dr. Chun-Hung Chen. OCBA determines the number of replications or the simulation time that is needed in order to receive acceptable or best results within a set of given parameters. This is accomplished by using an asymptotic framework to analyze the structure of the optimal allocation. OCBA has also been shown effective in enhancing partition-based random search algorithms for solving deterministic global optimization problems. 最优计算量分配（OCBA） 是最早由陈俊宏教授于90年代中期提出的一个概念。这一方法试图在找到一个最优决策的前提下最大化仿真效率。 简言之，OCBA是一种仿真方法，它能够在给定一组仿真参数的情况下，帮助确定所需的仿真次数及（或）所需的仿真时间，以达到可接受的（或最好的）结果。 其具体做法是通过使用一个渐进框架对最优分配的结构进行分析。
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