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State of the Art in Global Optimization

Computational Methods and Applications

  • C. A. Floudas
  • P. M. Pardalos

Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 7)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Erich Novak, Klaus Ritter
    Pages 19-33
  3. Shuzhong Shi, Quan Zheng, Deming Zhuang
    Pages 47-56
  4. K. G. Ramakrishnan, Mauricio G. C. Resende, Panos M. Pardalos
    Pages 57-73
  5. Pierluigi Maponi, Maria Cristina Recchioni, Francesco Zirilli
    Pages 109-117
  6. George H. Staus, Lorenz T. Biegler, B. Erik Ydstie
    Pages 119-137
  7. V. Visweswaran, C. A. Floudas, M. G. Ierapetritou, E. N. Pistikopoulos
    Pages 139-162
  8. Jacob Barhen, Vladimir Protopopescu
    Pages 163-180
  9. Diane Maclagan, Timothy Sturge, William Baritompa
    Pages 201-211
  10. Kristina Holmqvist, Athanasios Migdalas
    Pages 213-226
  11. D. W. Bulger, G. R. Wood
    Pages 227-233
  12. W. Edmonson, K. Srinivasan, C. Wang, J. Principe
    Pages 235-247
  13. Emanuel Falkenauer
    Pages 249-265
  14. I. P. Androulakis, V. Visweswaran, C. A. Floudas
    Pages 285-301
  15. J. Parker Shectman, Nikolaos V. Sahinidis
    Pages 303-339
  16. Tianbing Qian, Yinyu Ye, Panos M. Pardalos
    Pages 341-351
  17. Aimo Törn, Sami Viitanen
    Pages 353-363
  18. Jianming Shi, Yamamoto Yoshitsugu
    Pages 395-411
  19. P. Sussner, P. M. Pardalos, G. X. Ritter
    Pages 457-474
  20. J. A. Filar, P. S. Gaertner, M. A. Janssen
    Pages 475-498
  21. Linas Mockus, Gintaras V. Reklaitis
    Pages 521-538
  22. Angelo Lucia, Jinxian Xu
    Pages 539-561
  23. Julio R. Banga, Warren D. Seider
    Pages 563-583

About this book

Introduction

Optimization problems abound in most fields of science, engineering, and tech­ nology. In many of these problems it is necessary to compute the global optimum (or a good approximation) of a multivariable function. The variables that define the function to be optimized can be continuous and/or discrete and, in addition, many times satisfy certain constraints. Global optimization problems belong to the complexity class of NP-hard prob­ lems. Such problems are very difficult to solve. Traditional descent optimization algorithms based on local information are not adequate for solving these problems. In most cases of practical interest the number of local optima increases, on the aver­ age, exponentially with the size of the problem (number of variables). Furthermore, most of the traditional approaches fail to escape from a local optimum in order to continue the search for the global solution. Global optimization has received a lot of attention in the past ten years, due to the success of new algorithms for solving large classes of problems from diverse areas such as engineering design and control, computational chemistry and biology, structural optimization, computer science, operations research, and economics. This book contains refereed invited papers presented at the conference on "State of the Art in Global Optimization: Computational Methods and Applications" held at Princeton University, April 28-30, 1995. The conference presented current re­ search on global optimization and related applications in science and engineering. The papers included in this book cover a wide spectrum of approaches for solving global optimization problems and applications.

Keywords

algorithms chemistry computer design genetic algorithms global optimization linear optimization model modeling nonlinear optimization operations research optimization programming research scheduling

Editors and affiliations

  • C. A. Floudas
    • 1
  • P. M. Pardalos
    • 2
  1. 1.Princeton UniversityUSA
  2. 2.University of FloridaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-3437-8
  • Copyright Information Springer-Verlag US 1996
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-3439-2
  • Online ISBN 978-1-4613-3437-8
  • Series Print ISSN 1571-568X
  • Buy this book on publisher's site
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