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Global Optimization with Non-Convex Constraints

Sequential and Parallel Algorithms

  • Roman G. Strongin
  • Yaroslav D. Sergeyev

Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 45)

Table of contents

  1. Front Matter
    Pages i-xxviii
  2. Global Optimization Algorithms as Decision Procedures. Theoretical Background and Core Univariate Case

    1. Front Matter
      Pages 1-1
    2. Roman G. Strongin, Yaroslav D. Sergeyev
      Pages 3-51
    3. Roman G. Strongin, Yaroslav D. Sergeyev
      Pages 127-229
    4. Roman G. Strongin, Yaroslav D. Sergeyev
      Pages 231-315
  3. Generalizations for Parallel Computing, Constrained and Multiple Criteria Problems

    1. Front Matter
      Pages 317-317
    2. Roman G. Strongin, Yaroslav D. Sergeyev
      Pages 379-418
    3. Roman G. Strongin, Yaroslav D. Sergeyev
      Pages 419-441
  4. Global Optimization in Many Dimensions. Generalizations through Peano Curves

    1. Front Matter
      Pages 443-443
    2. Roman G. Strongin, Yaroslav D. Sergeyev
      Pages 445-549
    3. Roman G. Strongin, Yaroslav D. Sergeyev
      Pages 551-610
    4. Roman G. Strongin, Yaroslav D. Sergeyev
      Pages 611-649
  5. Back Matter
    Pages 651-703

About this book

Introduction

Everything should be made as simple as possible, but not simpler. (Albert Einstein, Readers Digest, 1977) The modern practice of creating technical systems and technological processes of high effi.ciency besides the employment of new principles, new materials, new physical effects and other new solutions ( which is very traditional and plays the key role in the selection of the general structure of the object to be designed) also includes the choice of the best combination for the set of parameters (geometrical sizes, electrical and strength characteristics, etc.) concretizing this general structure, because the Variation of these parameters ( with the structure or linkage being already set defined) can essentially affect the objective performance indexes. The mathematical tools for choosing these best combinations are exactly what is this book about. With the advent of computers and the computer-aided design the pro­ bations of the selected variants are usually performed not for the real examples ( this may require some very expensive building of sample op­ tions and of the special installations to test them ), but by the analysis of the corresponding mathematical models. The sophistication of the mathematical models for the objects to be designed, which is the natu­ ral consequence of the raising complexity of these objects, greatly com­ plicates the objective performance analysis. Today, the main (and very often the only) available instrument for such an analysis is computer­ aided simulation of an object's behavior, based on numerical experiments with its mathematical model.

Keywords

Computer STATISTICA algorithms computer science global optimization mathematics optimization science

Authors and affiliations

  • Roman G. Strongin
    • 1
  • Yaroslav D. Sergeyev
    • 1
    • 2
  1. 1.Nizhni Novgorod State UniversityNizhni NovgorodRussia
  2. 2.Institute of Systems Analysis and Information TechnologyUniversity of CalabriaRendeItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-4677-1
  • Copyright Information Springer-Verlag US 2000
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-7117-5
  • Online ISBN 978-1-4615-4677-1
  • Series Print ISSN 1571-568X
  • Buy this book on publisher's site
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