Articles liés à Aggregation in Large-Scale Optimization
Synopsis
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 passes to the next step. Macrovariables are commonly used in coordinating problems of hierarchical optimization.
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- ÉditeurSpringer-Verlag New York Inc.
- Date d'édition2003
- ISBN 10 1402075979
- ISBN 13 9781402075971
- ReliureRelié
- Langueanglais
- Nombre de pages291
- Coordonnées du fabricantnon disponible
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Aggregation in Large-Scale Optimization
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Aggregation in Large-Scale Optimization
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Aggregation In Large-scale Optimization
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Aggregation in Large-Scale Optimization (Applied Optimization, 83)
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Aggregation in Large-Scale Optimization
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Springer US, Springer US, 2003
ISBN 10 : 1402075979
ISBN 13 : 9781402075971
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 passes to the next step. Macrovariables are commonly used in coordinating problems of hierarchical optimization. N° de réf. du vendeur 9781402075971
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Aggregation in Large-Scale Optimization
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Aggregation in Large-Scale Optimization
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Springer-Verlag New York Inc., 2003
ISBN 10 : 1402075979
ISBN 13 : 9781402075971
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Aggregation in Large-Scale Optimization
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Springer US Sep 2003, 2003
ISBN 10 : 1402075979
ISBN 13 : 9781402075971
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 passes to the next step. Macrovariables are commonly used in coordinating problems of hierarchical optimization. 304 pp. Englisch. N° de réf. du vendeur 9781402075971
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