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Approximation Methods for Polynomial Optimization

Models, Algorithms, and Applications

Approximation Methods for Polynomial Optimization by Zhening Li
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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.

Springer New York; July 2012
128 pages; ISBN 9781461439844
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Title: Approximation Methods for Polynomial Optimization
Author: Zhening Li; Simai He; Shuzhong Zhang
 
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