Overview

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.

Full Product Details

Author:   Jonas Mockus ,  William Eddy ,  Gintaras Reklaitis
Publisher:   Springer
Imprint:   Springer
Edition:   1997 ed.
Volume:   17
Dimensions:   Width: 15.60cm , Height: 2.30cm , Length: 23.40cm
Weight:   1.690kg
ISBN:  

9780792343271

ISBN 10:   0792343271
Pages:   397
Publication Date:   31 December 1996
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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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.

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