Nonconvex Optimization and Its Applications

Bayesian Heuristic Approach to Discrete and Global Optimization

Algorithms, Visualization, Software, and Applications

Authors: Mockus, Jonas, Eddy, William, Reklaitis, Gintaras

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About this book

Bayesian decision theory is known to provide an effective framework for the practical solution of discrete and nonconvex optimization problems. This book is the first to demonstrate that this framework is also well suited for the exploitation of heuristic methods in the solution of such problems, especially those of large scale for which exact optimization approaches can be prohibitively costly. The book covers all aspects ranging from the formal presentation of the Bayesian Approach, to its extension to the Bayesian Heuristic Strategy, and its utilization within the informal, interactive Dynamic Visualization strategy. The developed framework is applied in forecasting, in neural network optimization, and in a large number of discrete and continuous optimization problems. Specific application areas which are discussed include scheduling and visualization problems in chemical engineering, manufacturing process control, and epidemiology. Computational results and comparisons with a broad range of test examples are presented. The software required for implementation of the Bayesian Heuristic Approach is included. Although some knowledge of mathematical statistics is necessary in order to fathom the theoretical aspects of the development, no specialized mathematical knowledge is required to understand the application of the approach or to utilize the software which is provided.
Audience: The book is of interest to both researchers in operations research, systems engineering, and optimization methods, as well as applications specialists concerned with the solution of large scale discrete and/or nonconvex optimization problems in a broad range of engineering and technological fields. It may be used as supplementary material for graduate level courses.

Table of contents (21 chapters)

  • Different Approaches to Numerical Techniques and Different Ways of Regarding Heuristics: Possibilities and Limitations

    Mockus, Jonas (et al.)

    Pages 3-29

  • Information-Based Complexity (IBC) and the Bayesian Heuristic Approach

    Mockus, Jonas (et al.)

    Pages 31-46

  • Mathematical Justification of the Bayesian Heuristics Approach

    Mockus, Jonas (et al.)

    Pages 47-59

  • Bayesian Approach to Continuous Global and Stochastic Optimization

    Mockus, Jonas (et al.)

    Pages 63-69

  • Examples of Continuous Optimization

    Mockus, Jonas (et al.)

    Pages 71-82

Buy this book

eBook $219.00
price for USA in USD (gross)
  • ISBN 978-1-4757-2627-5
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $279.99
price for USA in USD
  • ISBN 978-0-7923-4327-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $279.99
price for USA in USD
  • ISBN 978-1-4419-4767-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Bayesian Heuristic Approach to Discrete and Global Optimization
Book Subtitle
Algorithms, Visualization, Software, and Applications
Authors
Series Title
Nonconvex Optimization and Its Applications
Series Volume
17
Copyright
1997
Publisher
Springer US
Copyright Holder
Springer Science+Business Media Dordrecht
eBook ISBN
978-1-4757-2627-5
DOI
10.1007/978-1-4757-2627-5
Hardcover ISBN
978-0-7923-4327-1
Softcover ISBN
978-1-4419-4767-3
Series ISSN
1571-568X
Edition Number
1
Number of Pages
XV, 397
Topics