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Global Optimization in Action: Continuous and Lipschitz Optimization : Algorithms, Implementations and Applications
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Bibliographic Detail
Publisher Springer Verlag
Publication date June 1, 1996
Pages 478
Binding Hardcover
Book category Adult Non-Fiction
ISBN-13 9780792337577
ISBN-10 0792337573
Dimensions 1.25 by 6.75 by 10 in.
Weight 2.30 lbs.
Published in Europe
Original list price $409.00
Other format details sci/tech
Summaries and Reviews
Amazon.com description: Product Description: In science, engineering and economics, decision problems are frequently modelled by optimizing the value of a (primary) objective function under stated feasibility constraints. In many cases of practical relevance, the optimization problem structure does not warrant the global optimality of local solutions; hence, it is natural to search for the globally best solution(s).
Global Optimization in Action provides a comprehensive discussion of adaptive partition strategies to solve global optimization problems under very general structural requirements. A unified approach to numerous known algorithms makes possible straightforward generalizations and extensions, leading to efficient computer-based implementations. A considerable part of the book is devoted to applications, including some generic problems from numerical analysis, and several case studies in environmental systems analysis and management. The book is essentially self-contained and is based on the author's research, in cooperation (on applications) with a number of colleagues.
Audience: Professors, students, researchers and other professionals in the fields of operations research, management science, industrial and applied mathematics, computer science, engineering, economics and the environmental sciences.

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Hardcover
Book cover for 9780792337577
 
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from Springer Verlag (June 1, 1996)
9780792337577 | details & prices | 478 pages | 6.75 × 10.00 × 1.25 in. | 2.30 lbs | List price $409.00
About: In science, engineering and economics, decision problems are frequently modelled by optimizing the value of a (primary) objective function under stated feasibility constraints.

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