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Bayesian Heuristic Approach to Discrete and Global Optimization

Algorithms, Visualization, Software, and Applications

  • Jonas Mockus
  • William Eddy
  • Audris Mockus
  • Linas Mockus
  • Gintaras Reklaitis

Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 17)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Bayesian Approach

    1. Front Matter
      Pages 1-1
    2. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 3-29
    3. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 31-46
    4. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 47-59
  3. Global Optimization

    1. Front Matter
      Pages 61-61
    2. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 63-69
    3. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 71-82
    4. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 83-117
    5. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 119-127
  4. Networks Optimization

    1. Front Matter
      Pages 129-129
    2. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 131-138
    3. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 139-151
    4. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 153-174
  5. Discrete Optimization

    1. Front Matter
      Pages 175-175
    2. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 177-194
    3. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 195-219
    4. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 221-230
  6. Batch Process Scheduling

    1. Front Matter
      Pages 231-231
    2. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 233-244
    3. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 245-259
    4. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 261-274
  7. Software for Global Optimization

    1. Front Matter
      Pages 275-275
    2. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 277-282
    3. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 283-325
    4. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 327-336
    5. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 337-346
  8. Visualization

    1. Front Matter
      Pages 347-347
    2. Jonas Mockus, William Eddy, Audris Mockus, Linas Mockus, Gintaras Reklaitis
      Pages 349-377
  9. Back Matter
    Pages 379-397

About this book

Introduction

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.

Keywords

Fathom algorithm algorithms chemical engineering decision theory genetic algorithms global optimization linear optimization mathematical statistics network nonlinear optimization operations research optimization scheduling statistics

Authors and affiliations

  • Jonas Mockus
    • 1
    • 2
    • 3
  • William Eddy
    • 4
  • Audris Mockus
    • 5
  • Linas Mockus
    • 6
  • Gintaras Reklaitis
    • 6
  1. 1.Institute of Mathematics and InformaticsKaunas Technological UniversityVilniusLithuania
  2. 2.Vytautas Magnus UniversityVilniusLithuania
  3. 3.Vilnius Technical UniversityVilniusLithuania
  4. 4.Department of StatisticsCarnegie-Mellon UniversityPittsburghUSA
  5. 5.Lucent Technologies AT&T Bell LaboratoriesPittsburghUSA
  6. 6.School of Chemical EngineeringPurdue UniversityW. LafayetteUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-2627-5
  • Copyright Information Springer-Verlag US 1997
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-4767-3
  • Online ISBN 978-1-4757-2627-5
  • Series Print ISSN 1571-568X
  • Buy this book on publisher's site