High Performance Optimization

  • Hans Frenk
  • Kees Roos
  • Tamás Terlaky
  • Shuzhong Zhang

Part of the Applied Optimization book series (APOP, volume 33)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Theory and Algorithms of Semidefinite Programming

    1. Front Matter
      Pages 1-1
    2. Hans Frenk, Kees Roos, Tamás Terlaky, Shuzhong Zhang
      Pages 3-20
    3. Hans Frenk, Kees Roos, Tamás Terlaky, Shuzhong Zhang
      Pages 21-60
    4. Hans Frenk, Kees Roos, Tamás Terlaky, Shuzhong Zhang
      Pages 61-91
    5. Hans Frenk, Kees Roos, Tamás Terlaky, Shuzhong Zhang
      Pages 93-127
    6. Hans Frenk, Kees Roos, Tamás Terlaky, Shuzhong Zhang
      Pages 129-141
    7. Hans Frenk, Kees Roos, Tamás Terlaky, Shuzhong Zhang
      Pages 143-155
    8. Hans Frenk, Kees Roos, Tamás Terlaky, Shuzhong Zhang
      Pages 157-194
  3. Linear, Quadratic, Semidefinite Programming and Beyond

    1. Front Matter
      Pages 195-195
    2. Katsuki Fujisawa, Mituhiro Fukuda, Masakazu Kojima, Kazuhide Nakata
      Pages 267-301
    3. Aharon Ben-Tal, Tamar Margalit, Arkadi Nemirovski
      Pages 303-328
    4. Zhi-Quan Luo, Jos F. Sturm
      Pages 383-404

About this book

Introduction

For a long time the techniques of solving linear optimization (LP) problems improved only marginally. Fifteen years ago, however, a revolutionary discovery changed everything. A new `golden age' for optimization started, which is continuing up to the current time. What is the cause of the excitement? Techniques of linear programming formed previously an isolated body of knowledge. Then suddenly a tunnel was built linking it with a rich and promising land, part of which was already cultivated, part of which was completely unexplored. These revolutionary new techniques are now applied to solve conic linear problems. This makes it possible to model and solve large classes of essentially nonlinear optimization problems as efficiently as LP problems. This volume gives an overview of the latest developments of such `High Performance Optimization Techniques'. The first part is a thorough treatment of interior point methods for semidefinite programming problems. The second part reviews today's most exciting research topics and results in the area of convex optimization.
Audience: This volume is for graduate students and researchers who are interested in modern optimization techniques.

Keywords

Finite algorithms calculus function linear optimization nonlinear optimization optimization proof

Editors and affiliations

  • Hans Frenk
    • 1
  • Kees Roos
    • 2
  • Tamás Terlaky
    • 2
  • Shuzhong Zhang
    • 1
  1. 1.Erasmus UniversityRotterdamThe Netherlands
  2. 2.Delft University of TechnologyThe Netherlands

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-3216-0
  • Copyright Information Springer-Verlag US 2000
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4419-4819-9
  • Online ISBN 978-1-4757-3216-0
  • Series Print ISSN 1384-6485
  • About this book