Description: Bayesian Heuristic Approach to Discrete and Global Optimization 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. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description Bayesian decision theory is known to provide an effective framework for the practical solution of discrete and nonconvex optimization problems. This text demonstrates that this framework is also well suited for the exploitation of heuristic methods in the solution of such problems, especially large scale ones for which exact optimization approaches can be prohibitively costly. It 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.The book should be 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. Table of Contents I Bayesian Approach.- 1 Different Approaches to Numerical Techniques and Different Ways of Regarding Heuristics: Possibilities and Limitations.- 2 Information-Based Complexity (IBC) and the Bayesian Heuristic Approach.- 3 Mathematical Justification of the Bayesian Heuristics Approach.- II Global Optimization.- 4 Bayesian Approach to Continuous Global and Stochastic Optimization.- 5 Examples of Continuous Optimization.- 6 Long-Memory Processes and Exchange Rate Forecasting.- 7 Optimization Problems in Simple Competitive Model.- III Networks Optimization.- 8 Application of Global Line-Search in the Optimization of Networks.- 9 Solving Differential Equations by Event- Driven Techniques for Parameter Optimization.- 10 Optimization in Neural Networks.- IV Discrete Optimization.- 11 Bayesian Approach to Discrete Optimization.- 12 Examples of Discrete Optimization.- 13 Application of BHA to Mixed Integer Nonlinear Programming (MINLP).- V Batch Process Scheduling.- 14 Batch/Semi-Continuous Process Scheduling Using MRP Heuristics.- 15 Batch Process Scheduling Using Simulated Annealing.- 16 Genetic Algorithms for BATCH Process Scheduling Using BHA and MILP Formulation.- VI Software for Global Optimization.- 17 Introduction to Global Optimization Software (GM).- 18 Portable Fortran Library for Continuous Global Optimization.- 19 Software for Continuous Global Optimization Using Unix C++.- 20 Examples of Unix C++ Software Applications.- VII Visualization.- 21 Dynamic Visualization in Modeling and Optimization of Ill Defined Problems: Case Studies and Generalizations.- References. Long Description 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. Details ISBN0792343271 Author Gintaras Reklaitis Short Title BAYESIAN HEURISTIC APPROACH TO Language English ISBN-10 0792343271 ISBN-13 9780792343271 Media Book Format Hardcover DEWEY 519.3 Series Number 17 Year 1996 Publication Date 1996-12-31 Subtitle Algorithms, Visualization, Software, and Applications Place of Publication Dordrecht Publisher Springer Pages 397 Imprint Springer Country of Publication Netherlands DOI 10.1007/b129426 Edition Description 1997 ed. Series Nonconvex Optimization and Its Applications Edition 1997th Alternative 9781441947673 Illustrations XV, 397 p. Audience Professional & Vocational We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:96272189;
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ISBN-13: 9780792343271
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
Publication Year: 1996
Subject: Mathematics
Item Height: 234 mm
Item Weight: 1690 g
Type: Textbook
Author: William Eddy, Jonas Mockus, Gintaras Reklaitis
Item Width: 156 mm
Format: Hardcover
