Approximation Methods for Polynomial Optimization [electronic resource] : Models, Algorithms, and Applications / by Zhening Li, Simai He, Shuzhong Zhang.

By:

Li, Zhening [author.]

Contributor(s):

Series: SpringerBriefs in OptimizationPublisher: New York, NY : Springer New York : Imprint: Springer, 2012Description: VIII, 124 p. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9781461439844

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No titleDDC classification:

519.6 23

LOC classification:

QA402.5-402.6

Online resources:

Click here to access online

Contents:

1. Introduction.-2. Polynomial over the Euclidean Ball -- 3. Extensions of the Constraint Sets -- 4. Applications -- 5. Concluding Remarks.

In: Springer eBooksSummary: 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.

Item type:

eBooks

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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.