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＜電子ブック＞
Approximation Methods for Polynomial Optimization : Models, Algorithms, and Applications / by Zhening Li, Simai He, Shuzhong Zhang
(SpringerBriefs in Optimization. ISSN:2191575X)

版	1st ed. 2012.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2012
本文言語	英語
大きさ	VIII, 124 p : online resource
著者標目	*Li, Zhening author He, Simai author Zhang, Shuzhong author SpringerLink (Online service)
件　名	LCSH:Mathematical optimization LCSH:Mathematical models LCSH:Algorithms LCSH:Mathematics FREE:Optimization FREE:Mathematical Modeling and Industrial Mathematics FREE:Algorithms FREE:Applications of Mathematics
一般注記	1.  Introduction.-2. Polynomial over the Euclidean Ball -- 3. Extensions of the Constraint Sets -- 4. Applications -- 5. Concluding Remarks 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 HTTP:URL=https://doi.org/10.1007/978-1-4614-3984-4

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