Aggregation in Large-Scale Optimization

Litvinchev, Igor (Computing Center, Russian Academy of Sciences, Moscow, Russia); Tsurkov, Vladimir

ISBN 10: 1402075979 / ISBN 13: 9781402075971

Published by Kluwer Academic Publishers, 2003

About this Item

Contains exact, approximate and iterative aggregation in large-scale optimization. This volume is suitable for specialists in operations research, optimization, and optimal control. Series: Applied Optimization. Num Pages: 291 pages, biography. BIC Classification: PBU. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 19. Weight in Grams: 609. . 2003. Hardback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9781402075971

Bibliographic Details

• Title: Aggregation in Large-Scale Optimization
• Publisher: Kluwer Academic Publishers
• Publication Date: 2003
• Language: English
• ISBN 10: 1402075979
• ISBN 13: 9781402075971
• Binding: Hardcover
• Condition: New

About this title

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.