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

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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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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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.
Content:
Front Matter....Pages i-xv
Front Matter....Pages 1-1
Different Approaches to Numerical Techniques and Different Ways of Regarding Heuristics: Possibilities and Limitations....Pages 3-29
Information-Based Complexity (IBC) and the Bayesian Heuristic Approach....Pages 31-46
Mathematical Justification of the Bayesian Heuristics Approach....Pages 47-59
Front Matter....Pages 61-61
Bayesian Approach to Continuous Global and Stochastic Optimization....Pages 63-69
Examples of Continuous Optimization....Pages 71-82
Long-Memory Processes and Exchange Rate Forecasting....Pages 83-117
Optimization Problems in Simple Competitive Model....Pages 119-127
Front Matter....Pages 129-129
Application of Global Line-Search in the Optimization of Networks....Pages 131-138
Solving Differential Equations by Event- Driven Techniques for Parameter Optimization....Pages 139-151
Optimization in Neural Networks....Pages 153-174
Front Matter....Pages 175-175
Bayesian Approach to Discrete Optimization....Pages 177-194
Examples of Discrete Optimization....Pages 195-219
Application of BHA to Mixed Integer Nonlinear Programming (MINLP)....Pages 221-230
Front Matter....Pages 231-231
Batch/Semi-Continuous Process Scheduling Using MRP Heuristics....Pages 233-244
Batch Process Scheduling Using Simulated Annealing....Pages 245-259
Genetic Algorithms for BATCH Process Scheduling Using BHA and MILP Formulation....Pages 261-274
Front Matter....Pages 275-275
Introduction to Global Optimization Software (GM)....Pages 277-282
Portable Fortran Library for Continuous Global Optimization....Pages 283-325
Software for Continuous Global Optimization Using Unix C++....Pages 327-336
Examples of Unix C++ Software Applications....Pages 337-346
Back Matter....Pages 379-397
Dynamic Visualization in Modeling and Optimization of Ill Defined Problems: Case Studies and Generalizations....Pages 349-377

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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.
Content:
Front Matter....Pages i-xv
Front Matter....Pages 1-1
Different Approaches to Numerical Techniques and Different Ways of Regarding Heuristics: Possibilities and Limitations....Pages 3-29
Information-Based Complexity (IBC) and the Bayesian Heuristic Approach....Pages 31-46
Mathematical Justification of the Bayesian Heuristics Approach....Pages 47-59
Front Matter....Pages 61-61
Bayesian Approach to Continuous Global and Stochastic Optimization....Pages 63-69
Examples of Continuous Optimization....Pages 71-82
Long-Memory Processes and Exchange Rate Forecasting....Pages 83-117
Optimization Problems in Simple Competitive Model....Pages 119-127
Front Matter....Pages 129-129
Application of Global Line-Search in the Optimization of Networks....Pages 131-138
Solving Differential Equations by Event- Driven Techniques for Parameter Optimization....Pages 139-151
Optimization in Neural Networks....Pages 153-174
Front Matter....Pages 175-175
Bayesian Approach to Discrete Optimization....Pages 177-194
Examples of Discrete Optimization....Pages 195-219
Application of BHA to Mixed Integer Nonlinear Programming (MINLP)....Pages 221-230
Front Matter....Pages 231-231
Batch/Semi-Continuous Process Scheduling Using MRP Heuristics....Pages 233-244
Batch Process Scheduling Using Simulated Annealing....Pages 245-259
Genetic Algorithms for BATCH Process Scheduling Using BHA and MILP Formulation....Pages 261-274
Front Matter....Pages 275-275
Introduction to Global Optimization Software (GM)....Pages 277-282
Portable Fortran Library for Continuous Global Optimization....Pages 283-325
Software for Continuous Global Optimization Using Unix C++....Pages 327-336
Examples of Unix C++ Software Applications....Pages 337-346
Back Matter....Pages 379-397
Dynamic Visualization in Modeling and Optimization of Ill Defined Problems: Case Studies and Generalizations....Pages 349-377
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