Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains

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Éditeur :

Springer


Collection :

SpringerBriefs in Electrical and Computer Engineering

Paru le : 2020-09-09

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Description

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.
Pages
120 pages
Collection
SpringerBriefs in Electrical and Computer Engineering
Parution
2020-09-09
Marque
Springer
EAN papier
9783030566777
EAN PDF
9783030566784

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
12
Taille du fichier
2263 Ko
Prix
52,74 €
EAN EPUB
9783030566784

Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
12
Taille du fichier
10138 Ko
Prix
52,74 €