A Set of Examples of Global and Discrete Optimization : Applications of Bayesian Heuristic Approach

Jonas Mockus

ISBN 10: 0792363590 / ISBN 13: 9780792363590

Edité par Springer Us Jul 2000, 2000

Neuware - 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. N° de réf. du vendeur 9780792363590

Synopsis :

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.

Détails bibliographiques

Titre : A Set of Examples of Global and Discrete ...
Éditeur : Springer Us Jul 2000
Date d'édition : 2000
Reliure : Buch
Etat : Neu