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Titlebook: Aggregation in Large-Scale Optimization; Igor Litvinchev,Vladimir Tsurkov Book 2003 Springer Science+Business Media Dordrecht 2003 Optimal

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期刊全稱Aggregation in Large-Scale Optimization
影響因子2023Igor Litvinchev,Vladimir Tsurkov
視頻videohttp://file.papertrans.cn/152/151287/151287.mp4
學(xué)科分類Applied Optimization
圖書封面Titlebook: Aggregation in Large-Scale Optimization;  Igor Litvinchev,Vladimir Tsurkov Book 2003 Springer Science+Business Media Dordrecht 2003 Optimal
影響因子When analyzing systems with a large number of parameters, the dimen- sion of the original system may present insurmountable difficulties for the analysis. It may then be convenient to reformulate the original system in terms of substantially fewer aggregated variables, or macrovariables. In other words, an original system with an n-dimensional vector of states is reformulated as a system with a vector of dimension much less than n. The aggregated variables are either readily defined and processed, or the aggregated system may be considered as an approximate model for the orig- inal system. In the latter case, the operation of the original system can be exhaustively analyzed within the framework of the aggregated model, and one faces the problems of defining the rules for introducing macrovariables, specifying loss of information and accuracy, recovering original variables from aggregates, etc. We consider also in detail the so-called iterative aggregation approach. It constructs an iterative process, at· every step of which a macroproblem is solved that is simpler than the original problem because of its lower dimension. Aggregation weights are then updated, and the procedure passe
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1384-6485 the analysis. It may then be convenient to reformulate the original system in terms of substantially fewer aggregated variables, or macrovariables. In other words, an original system with an n-dimensional vector of states is reformulated as a system with a vector of dimension much less than n. The
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Aggregated Problem and Bounds for Aggregation,t of the original problem by a simpler one. In this chapter, we consider realizations of these steps for various classes of optimization problems. The main attention is focused on estimating the aggregation error, which does not require solving the original problem.
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Applied Optimizationhttp://image.papertrans.cn/a/image/151287.jpg
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https://doi.org/10.1007/978-1-4419-9154-6Optimal control; operations research; optimization
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Aggregation in Large-Scale Optimization978-1-4419-9154-6Series ISSN 1384-6485
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