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Entropy Optimization and Mathematical Programming

  • S.-C. Fang
  • J. R. Rajasekera
  • H.-S. J. Tsao

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 8)

Table of contents

  1. Front Matter
    Pages i-x
  2. S.-C. Fang, J. R. Rajasekera, H.-S. J. Tsao
    Pages 1-16
  3. S.-C. Fang, J. R. Rajasekera, H.-S. J. Tsao
    Pages 17-49
  4. S.-C. Fang, J. R. Rajasekera, H.-S. J. Tsao
    Pages 51-124
  5. S.-C. Fang, J. R. Rajasekera, H.-S. J. Tsao
    Pages 125-185
  6. S.-C. Fang, J. R. Rajasekera, H.-S. J. Tsao
    Pages 187-246
  7. S.-C. Fang, J. R. Rajasekera, H.-S. J. Tsao
    Pages 247-284
  8. S.-C. Fang, J. R. Rajasekera, H.-S. J. Tsao
    Pages 285-323
  9. Back Matter
    Pages 325-343

About this book

Introduction

Entropy optimization is a useful combination of classical engineering theory (entropy) with mathematical optimization. The resulting entropy optimization models have proved their usefulness with successful applications in areas such as image reconstruction, pattern recognition, statistical inference, queuing theory, spectral analysis, statistical mechanics, transportation planning, urban and regional planning, input-output analysis, portfolio investment, information analysis, and linear and nonlinear programming.
While entropy optimization has been used in different fields, a good number of applicable solution methods have been loosely constructed without sufficient mathematical treatment. A systematic presentation with proper mathematical treatment of this material is needed by practitioners and researchers alike in all application areas. The purpose of this book is to meet this need. Entropy Optimization and Mathematical Programming offers perspectives that meet the needs of diverse user communities so that the users can apply entropy optimization techniques with complete comfort and ease. With this consideration, the authors focus on the entropy optimization problems in finite dimensional Euclidean space such that only some basic familiarity with optimization is required of the reader.

Keywords

Optimization Methods Pattern Recognition linear optimization nonlinear optimization optimization

Authors and affiliations

  • S.-C. Fang
    • 1
  • J. R. Rajasekera
    • 2
  • H.-S. J. Tsao
    • 3
  1. 1.North Carolina State UniversityRaleighUSA
  2. 2.International University of JapanUrasa, NiigataJapan
  3. 3.University of CaliforniaBerkeleyUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-6131-6
  • Copyright Information Kluwer Academic Publishers 1997
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-7810-5
  • Online ISBN 978-1-4615-6131-6
  • Series Print ISSN 0884-8289
  • Buy this book on publisher's site
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