Bayesian Heuristic Approach to Discrete and Global Optimization Algorithms, Visualization, Software, and Applications

Bayesian Heuristic Approach to Discrete and Global Optimization Algorithms, Visualization, Software, and Applications / by Jonas Mockus, William Eddy, Gintaras Reklaitis.

Bayesian decision theory is known to provide an effective framework for the practical solution of discrete and nonconvex optimization problems. This book is the first to demonstrate that this framework is also well suited for the exploitation of heuristic methods in the solution of such problems, es...

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Bibliographic Details
Main Authors: Mockus, Jonas (Author), Eddy, William (Author), Reklaitis, Gintaras (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY : Springer US : Imprint: Springer, 1997.
Edition:1st ed. 1997.
Series:Nonconvex Optimization and Its Applications, 17
Springer eBook Collection.
Subjects:
Combinatorics.
Mathematical optimization.
Statistics .
Applied mathematics.
Engineering mathematics.
Electronic resources (E-books)
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Call Number: E-Book
Status: Available

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