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Autor(en): 
  • Anatoly Zhigljavsky
  • Antanas ¿Ilinskas
  • Bayesian and High-Dimensional Global Optimization 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 5-10 Tagen versandfertig
    Veröffentlichung:  März 2021  
    Genre:  Schulbücher 
     
    Algebra / Bayesianglobaloptimization / Expensiveblack-boxfunctions / Fertigungstechnik und Ingenieurwesen / globaloptimization / Globalrandomsearch / High-dimensionaloptimization / MatrixTheory
    ISBN:  9783030647117 
    EAN-Code: 
    9783030647117 
    Verlag:  Palgrave Macmillan 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D 8 mm 
    Gewicht:  207 gr 
    Seiten:  128 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Accessible to a variety of readers, this book is of interest to specialists, graduate students and researchers in mathematics, optimization, computer science, operations research, management science, engineering and other applied areas interested in solving optimization problems. Basic principles, potential and boundaries of applicability of stochastic global optimization techniques are examined in this book. A variety of issues that face specialists in global optimization are explored, such as multidimensional spaces which are frequently ignored by researchers. The importance of precise interpretation of the mathematical results in assessments of optimization methods is demonstrated through examples of convergence in probability of random search. Methodological issues concerning construction and applicability of stochastic global optimization methods are discussed, including the one-step optimal average improvement method based on a statistical model of the objective function. A significant portion of this book is devoted to an analysis of high-dimensional global optimization problems and the so-called 'curse of dimensionality'. An examination of the three different classes of high-dimensional optimization problems, the geometry of high-dimensional balls and cubes, very slow convergence of global random search algorithms in large-dimensional problems , and poor uniformity of the uniformly distributed sequences of points are included in this book.

      



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