Bayesian Heuristic Approach to Discrete and Global Optimization

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. Language: English