Approximation Methods for Polynomial Optimization: Models, Algorithms, and Applications

✍ Scribed by Zhening Li, Simai He, Shuzhong Zhang (auth.)

• Publisher: Springer-Verlag New York
• Year: 2012
• Tongue: English
• Leaves: 129
• Series: SpringerBriefs in optimization
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-viii
Introduction....Pages 1-22
Polynomial Optimization Over the Euclidean Ball....Pages 23-51
Extensions of the Constraint Sets....Pages 53-97
Applications....Pages 99-111
Concluding Remarks....Pages 113-117
Back Matter....Pages 119-124

✦ Subjects

Optimization; Mathematical Modeling and Industrial Mathematics; Algorithms; Applications of Mathematics