This book shows how the Bayesian Approach (BA) improves well known heuristics by randomizing and optimizing their parameters. That is the Bayesian Heuristic Approach (BHA). The ten in-depth examples are designed to teach Operations Research using Internet. Each example is a simple representation of some impor tant family of real-life problems. The accompanying software can be run by remote Internet users. The supporting web-sites include software for Java, C++, and other lan guages. A theoretical setting is described in which one can discuss a Bayesian adaptive choice of heuristics for discrete and global optimization prob lems. The techniques are evaluated in the spirit of the average rather than the worst case analysis. In this context, "heuristics" are understood to be an expert opinion defining how to solve a family of problems of dis crete or global optimization. The term "Bayesian Heuristic Approach" means that one defines a set of heuristics and fixes some prior distribu tion on the results obtained. By applying BHA one is looking for the heuristic that reduces the average deviation from the global optimum. The theoretical discussions serve as an introduction to examples that are the main part of the book. All the examples are interconnected. Dif ferent examples illustrate different points of the general subject. How ever, one can consider each example separately, too.
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Preface. Part I: About the Bayesian Approach. 1. General Ideas. 2. Explaining BHA by Knapsack Example. Part II: Software for Global Optimization. 3. Introduction. 4. Fortran. 5. Turbo C. 6. C++. 7. Java 1.0. 8. Java 1.2. Part III: Examples of Models. 9. Nash Equilibrium. 10. Walras Equilibrium. 11. Inspection Model. 12. Differential Game. 13. Investment Problem. 14. Exchange Rate Prediction. 15. Call Centers. 16. Optimal Scheduling. 17. Sequential Decisions. References. Index.
Book by Mockus Jonas
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Hardcover. Condizione: new. Hardcover. This text shows how to improve well-known heuristics by randomizing and optimizing their parameters. The ten in-depth examples are designed to teach operations research and the theory of games and markets using the Internet. Each example is a simple representation of some important family of real-life problems. Remote Internet users can run the accompanying software. The supporting web sites include software for Java, C++, and other languages. Researchers and specialists in operations research, systems engineering and optimization methods, as well as Internet applications experts in the fields of economics, industrial and applied mathematics, computer science, engineering, and environmental sciences should find this work useful. This book shows how the Bayesian Approach (BA) improves well known heuristics by randomizing and optimizing their parameters. A theoretical setting is described in which one can discuss a Bayesian adaptive choice of heuristics for discrete and global optimization prob lems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780792363590
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Condizione: New. This text shows how to improve well-known heuristics by randomizing and optimizing their parameters. The ten in-depth examples are designed to teach operations research and the theory of games and markets using the Internet. Series: Applied Optimization. Num Pages: 336 pages, biography. BIC Classification: GPFC; PBV. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 235 x 155 x 20. Weight in Grams: 653. . 2000. Hardback. . . . . Codice articolo V9780792363590
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