Parallel Hierarchical Methods for Complex Systems Optimization

机译：复杂系统优化的并行层次方法

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The paper is concerned with computational research for large scale systems. The focus is on the hierarchical optimization methods that can be successfully applied to large scale optimization problems. A key issue is the possibility of solving several less dimension problems instead of one global high dimension task. Particular emphasis is laid on coarse granularity parallel implementation and its effectiveness. The paper discusses the usage of price coordination for real-life systems optimization. The results of numerical experiments performed for mean-variance portfolio selection using cluster of computers are presented and discussed.

机译：本文涉及大规模系统的计算研究。重点是可以成功应用于大规模优化问题的分层优化方法。一个关键问题是解决几个较小维度问题而不是一项全局高维度任务的可能性。特别强调的是粗粒度并行实现及其有效性。本文讨论了价格协调在实际系统优化中的用法。提出并讨论了使用计算机集群进行均方差投资组合选择的数值实验结果。

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来源
《International Conference on Computational Science and Its Applications(ICCSA 2006) pt.1; 20060508-11; Glasgow(GB)》|2006年|P.537-546|共10页
会议地点 Glasgow(GB)
作者
Ewa Niewiadomska-Szynkiewicz;
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作者单位

Institute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland;

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会议组织
原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
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机译：一种基于复杂系统分层控制方法的多学科耦合关系协调算法及其在多学科设计优化中的应用

Parallel Hierarchical Methods for Complex Systems Optimization

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