Approximation Methods for Polynomial Optimization

Models, Algorithms, and Applications

  • Zhening Li
  • Simai He
  • Shuzhong Zhang

Part of the SpringerBriefs in Optimization book series (BRIEFSOPTI)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Zhening Li, Simai He, Shuzhong Zhang
    Pages 1-22
  3. Zhening Li, Simai He, Shuzhong Zhang
    Pages 23-51
  4. Zhening Li, Simai He, Shuzhong Zhang
    Pages 53-97
  5. Zhening Li, Simai He, Shuzhong Zhang
    Pages 99-111
  6. Zhening Li, Simai He, Shuzhong Zhang
    Pages 113-117
  7. Back Matter
    Pages 119-124

About this book


Polynomial optimization have been a hot research topic for the past few years and its applications range from Operations Research, biomedical engineering, investment science, to quantum mechanics, linear algebra, and signal processing, among many others. In this brief the authors discuss some important subclasses of polynomial optimization models arising from various applications, with a focus on approximations algorithms with guaranteed worst case performance analysis. The brief presents a clear view of the basic ideas underlying the design of such algorithms and the benefits are highlighted by illustrative examples showing the possible applications.


This timely treatise will appeal to researchers and graduate students in the fields of optimization, computational mathematics, Operations Research, industrial engineering, and computer science.


Approximation algorithm approximation ratio binary integer programming mixed integer programming nonlinear programming polynomial optimization problem

Authors and affiliations

  • Zhening Li
    • 1
  • Simai He
    • 2
  • Shuzhong Zhang
    • 3
  1. 1., MathematicsShanghai UniversityShanghaiChina, People's Republic
  2. 2., Management SciencesCity University of Hong KongKowloon TongHong Kong SAR
  3. 3., Industrial & Systems EngineeringUniversity of MinnesotaMinneapolisUSA

Bibliographic information

  • DOI
  • Copyright Information Zhening Li, Simai He,Shuzhong Zhang 2012
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-3983-7
  • Online ISBN 978-1-4614-3984-4
  • Series Print ISSN 2190-8354
  • Series Online ISSN 2191-575X
  • Buy this book on publisher's site