Description: Bayesian Heuristic Approach to Discrete and Global Optimization Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Algorithms, Visualization, Software, and Applications Author(s): Jonas Mockus, William Eddy, Gintaras Reklaitis Format: Paperback Publisher: Springer-Verlag New York Inc., United States Imprint: Springer-Verlag New York Inc. ISBN-13: 9781441947673, 978-1441947673 Synopsis 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, especially those of large scale for which exact optimization approaches can be prohibitively costly. The book covers all aspects ranging from the formal presentation of the Bayesian Approach, to its extension to the Bayesian Heuristic Strategy, and its utilization within the informal, interactive Dynamic Visualization strategy. The developed framework is applied in forecasting, in neural network optimization, and in a large number of discrete and continuous optimization problems. Specific application areas which are discussed include scheduling and visualization problems in chemical engineering, manufacturing process control, and epidemiology. Computational results and comparisons with a broad range of test examples are presented. The software required for implementation of the Bayesian Heuristic Approach is included. Although some knowledge of mathematical statistics is necessary in order to fathom the theoretical aspects of the development, no specialized mathematical knowledge is required to understand the application of the approach or to utilize the software which is provided. Audience: The book is of interest to both researchers in operations research, systems engineering, and optimization methods, as well as applications specialists concerned with the solution of large scale discrete and/or nonconvex optimization problems in a broad range of engineering and technological fields. It may be used as supplementary material for graduate level courses.
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Book Title: Bayesian Heuristic Approach to Discrete and Global Optimization
Number of Pages: 397 Pages
Language: English
Publication Name: Bayesian Heuristic Approach to Discrete and Global Optimization: Algorithms, Visualization, Software, and Applications
Publisher: Springer-Verlag New York Inc.
Publication Year: 2010
Subject: Mathematics
Item Height: 235 mm
Item Weight: 635 g
Type: Textbook
Author: William Eddy, Gintaras Reklaitis, Jonas Mockus
Series: Nonconvex Optimization and Its Applications
Item Width: 155 mm
Format: Paperback
