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Bayesian Heuristic Approach to Discrete and Global Optimization : Algorithms, Visualization, Software, and Applications - Jonas Mockus

Bayesian Heuristic Approach to Discrete and Global Optimization

Algorithms, Visualization, Software, and Applications

Hardcover Published: 31st December 1996
ISBN: 9780792343271
Number Of Pages: 397

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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.

Preface
Bayesian Approachp. 1
Different Approaches to Numerical Techniques and Different Ways of Regarding Heuristics: Possibilities and Limitationsp. 3
Information-Based Complexity (IBC) and the Bayesian Heuristic Approachp. 31
Mathematical Justification of the Bayesian Heuristics Approachp. 47
Global Optimizationp. 61
Bayesian Approach to Continuous Global and Stochastic Optimizationp. 63
Examples of Continuous Optimizationp. 71
Long-Memory Processes and Exchange Rate Forecastingp. 83
Optimization Problems in Simple Competitive Modelp. 119
Networks Optimizationp. 129
Application of Global Line-Search in the Optimization of Networksp. 131
Solving Differential Equations by Event-Driven Techniques for Parameter Optimizationp. 139
Optimization in Neural Networksp. 153
Discrete Optimizationp. 175
Bayesian Approach to Discrete Optimizationp. 177
Examples of Discrete Optimizationp. 195
Application of BHA to Mixed Integer Nonlinear Programming (MINLP)p. 221
Batch Process Schedulingp. 231
Batch/Semi-Continuous Process Scheduling Using MRP Heuristicsp. 233
Batch Process Scheduling Using Simulated Annealingp. 245
Genetic Algorithms for Batch Process Scheduling Using BHA and MILP Formulationp. 261
Software for Global Optimizationp. 275
Introduction to Global Optimization Software (GM)p. 277
Portable Fortran Library for Continuous Global Optimizationp. 283
Software for Continuous Global Optimization Using UNIX C++p. 327
Examples of UNIX C++ Software Applicationsp. 337
Visualizationp. 347
Dynamic Visualization in Modeling and Optimization of Ill Defined Problems: Case Studies and Generalizationsp. 349
Referencesp. 379
Indexp. 393
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9780792343271
ISBN-10: 0792343271
Series: Nonconvex Optimization and Its Applications, V. 17
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 397
Published: 31st December 1996
Publisher: Springer
Country of Publication: NL
Dimensions (cm): 25.4 x 17.15  x 3.18
Weight (kg): 0.76