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Mathematics | Approximation Methods for Polynomial Optimization - Models, Algorithms, and Applications

Approximation Methods for Polynomial Optimization

Models, Algorithms, and Applications

Li, Zhening, He, Simai, Zhang, Shuzhong

2012, VIII, 124 p.

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  • Discuss some important subclasses of polynomial optimization models arising from various applications
  • Focuses on approximations algorithms with guaranteed worst case performance analysis
  • Presents a clear view of the basic ideas underlying the design of  algorithms and the benefits are highlighted by illustrative examples showing the possible applications

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.

Content Level » Research

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

Related subjects » Applications - Computational Science & Engineering - Mathematics

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