Foundations Of Average Cost Nonhomogeneous Controlled Markov Chains
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Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains
This Springer brief addresses the challenges encountered in the study of the optimization of time-nonhomogeneous Markov chains. It develops new insights and new methodologies for systems in which concepts such as stationarity, ergodicity, periodicity and connectivity do not apply. This brief introduces the novel concept of confluencity and applies a relative optimization approach. It develops a comprehensive theory for optimization of the long-run average of time-nonhomogeneous Markov chains. The book shows that confluencity is the most fundamental concept in optimization, and that relative optimization is more suitable for treating the systems under consideration than standard ideas of dynamic programming. Using confluencity and relative optimization, the author classifies states as confluent or branching and shows how the under-selectivity issue of the long-run average can be easily addressed, multi-class optimization implemented, and Nth biases and Blackwell optimality conditions derived. These results are presented in a book for the first time and so may enhance the understanding of optimization and motivate new research ideas in the area.
Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains
This Springer brief addresses the challenges encountered in the study of the optimization of time-nonhomogeneous Markov chains. It develops new insights and new methodologies for systems in which concepts such as stationarity, ergodicity, periodicity and connectivity do not apply. This brief introduces the novel concept of confluencity and applies a relative optimization approach. It develops a comprehensive theory for optimization of the long-run average of time-nonhomogeneous Markov chains. The book shows that confluencity is the most fundamental concept in optimization, and that relative optimization is more suitable for treating the systems under consideration than standard ideas of dynamic programming. Using confluencity and relative optimization, the author classifies states as confluent or branching and shows how the under-selectivity issue of the long-run average can be easily addressed, multi-class optimization implemented, and Nth biases and Blackwell optimality conditions derived. These results are presented in a book for the first time and so may enhance the understanding of optimization and motivate new research ideas in the area.
Social Informatics
Author: Anwitaman Datta
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-10-12
This book constitutes the proceedings of the Third International Conference on Social Informatics, SocInfo 2011, held in Singapore in October 2011. The 15 full papers, 8 short papers and 13 posters included in this volume were carefully reviewed and selected from 68 full paper and 13 poster submissions. The papers are organized in topical sections named: network analysis; eGovernance and knowledge management; applications of network analysis; community dynamics; case studies; trust, privacy and security; peer-production.