| 期刊全稱 | A Set of Examples of Global and Discrete Optimization | | 期刊簡稱 | Applications of Baye | | 影響因子2023 | Jonas Mockus | | 視頻video | http://file.papertrans.cn/143/142134/142134.mp4 | | 學(xué)科分類 | Applied Optimization | | 圖書封面 |  | | 影響因子 | 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 exampl | | Pindex | Book 2000 |
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