Table of contents

  1. Front Matter
    Pages i-xvii
  2. James Abello, Shankar Krishnan
    Pages 1-16
  3. Jens Albrecht, Dietmar Cieslik
    Pages 17-30
  4. Mikhail Andramonov, Jerzy Filar, Panos Pardalos, Alexander Rubinov
    Pages 31-47
  5. Francisco Barahona, Fabián A. Chudak
    Pages 48-62
  6. Immanuel M. Bomze, Marco Budinich, Marcello Pelillo, Claudio Rossi
    Pages 78-95
  7. Andreas Brieden, Peter Gritzmann, Victor Klee
    Pages 96-115
  8. Changhui Cris Choi, Yinyu Ye
    Pages 130-137
  9. Doug Cook, Gregory Hicks, Vance Faber, Madhav V. Marathe, Aravind Srinivasan, Yoram J. Sussmann et al.
    Pages 138-162
  10. Pierluigi Crescenzi, Xiaotie Deng, Christos H. Papadimitriou
    Pages 163-174
  11. Michael C. Ferris, Robert R. Meyer
    Pages 175-188
  12. Paola Festa, Raffaele Cerulli, Giancarlo Raiconi
    Pages 189-208
  13. Dimitris A. Fotakis, Paul G. Spirakis
    Pages 209-244
  14. John Franco
    Pages 245-286
  15. William W. Hager, Soon Chul Park, Timothy A. Davis
    Pages 298-307
  16. Harry B. Hunt III, Madhav V. Marathe, Richard E. Stearns
    Pages 308-322
  17. Klaus Jansen, Roberto Solis-Oba, Maxim Sviridenko
    Pages 338-346
  18. Marcello Pelillo, Kaleem Siddiqi, Steven W. Zucker
    Pages 422-445
  19. Akiko Takeda, Yang Dai, Mituhiro Fukuda, Masakazu Kojima
    Pages 489-510
  20. Sushil Verma, Peter A. Beling, Ilan Adler
    Pages 545-560
  21. Back Matter
    Pages 579-581

About this book


There has been much recent progress in approximation algorithms for nonconvex continuous and discrete problems from both a theoretical and a practical perspective. In discrete (or combinatorial) optimization many approaches have been developed recently that link the discrete universe to the continuous universe through geomet­ ric, analytic, and algebraic techniques. Such techniques include global optimization formulations, semidefinite programming, and spectral theory. As a result new ap­ proximate algorithms have been discovered and many new computational approaches have been developed. Similarly, for many continuous nonconvex optimization prob­ lems, new approximate algorithms have been developed based on semidefinite pro­ gramming and new randomization techniques. On the other hand, computational complexity, originating from the interactions between computer science and numeri­ cal optimization, is one of the major theories that have revolutionized the approach to solving optimization problems and to analyzing their intrinsic difficulty. The main focus of complexity is the study of whether existing algorithms are efficient for the solution of problems, and which problems are likely to be tractable. The quest for developing efficient algorithms leads also to elegant general approaches for solving optimization problems, and reveals surprising connections among problems and their solutions. A conference on Approximation and Complexity in Numerical Optimization: Con­ tinuous and Discrete Problems was held during February 28 to March 2, 1999 at the Center for Applied Optimization of the University of Florida.


algorithms complexity global optimization linear optimization metaheuristic optimization programming scheduling

Editors and affiliations

  • Panos M. Pardalos
    • 1
  1. 1.Center for Applied Optimization, Department of Industrial and Systems EngineeringUniversity of FloridaUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag US 2000
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-4829-8
  • Online ISBN 978-1-4757-3145-3
  • Series Print ISSN 1571-568X
  • Buy this book on publisher's site
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