Approximation Methods for Polynomial Optimization

Zhening Li

ISBN 10: 1461439833 / ISBN 13: 9781461439837

Published by Springer New York, Springer Jul 2012, 2012

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 132 pp. Englisch.

Seller Inventory # 9781461439837

Bibliographic Details

• Title: Approximation Methods for Polynomial Optimization
• Publisher: Springer New York, Springer Jul 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 1461439833
• ISBN 13: 9781461439837
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 8D millimeters 235 millimeters width by 155 millimeters height by 8 millimeters depth
• Weight: 213 grams

About this title

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.

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