Approximation Methods for Polynomial Optimization Models, Algorithms, and 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 import...

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Bibliographic Details
Main Authors: Li, Zhening, He, Simai (Author), Zhang, Shuzhong (Author)
Format: eBook
Language:English
Published: New York, NY Springer New York 2012, 2012
Edition:1st ed. 2012
Series:SpringerBriefs in Optimization
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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520 |a 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