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Autor(en): 
  • Vladimir Tsurkov
  • Large-scale Optimization: Problems and Methods 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  März 2001  
    Genre:  EDV / Informatik 
     
    Applications of Mathematics / Applied mathematics / B / Calculus of variations / Calculus of Variations and Optimal Control; Optimization / Calculus of Variations and Optimization / Control theory / Cybernetics and systems theory
    ISBN:  9780792368175 
    EAN-Code: 
    9780792368175 
    Verlag:  Springer EN 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  #51 - Applied Optimization  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  1400 gr 
    Seiten:  312 
    Illustration:  XII, 312 p. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Decomposition methods aim to reduce large-scale problems to simpler problems. This monograph presents selected aspects of the dimension-reduction problem. Exact and approximate aggregations of multidimensional systems are developed and from a known model of input-output balance, aggregation methods are categorized. The issues of loss of accuracy, recovery of original variables (disaggregation), and compatibility conditions are analyzed in detail. The method of iterative aggregation in large-scale problems is studied. For fixed weights, successively simpler aggregated problems are solved and the convergence of their solution to that of the original problem is analyzed. An introduction to block integer programming is considered. Duality theory, which is widely used in continuous block programming, does not work for the integer problem. A survey of alternative methods is presented and special attention is given to combined methods of decomposition. Block problems in which the coupling variables do not enter the binding constraints are studied. These models are worthwhile because they permit a decomposition with respect to primal and dual variables by two-level algorithms instead of three-level algorithms.
    Audience: This book is addressed to specialists in operations research, optimization, and optimal control.
      



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