Electronic Resource

Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
1908001545	EB0002151	519.233 Cao f	Central Library (OPAC)	Available

Detail Information

Series Title

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Call Number

519.233 Cao f

Publisher

Switzerland : Springer Cham., 2020

Collation

viii, 120p.:Ill

Language

English

ISBN/ISSN

978-3-030-56678-4

Classification

519.233

Content Type

Ebook

Media Type

-

Carrier Type

online resource

Edition

1

Subject(s)

Markov Processes

Specific Detail Info

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Statement of Responsibility

BRF

Other version/related

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