Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains (Paperback, 1st ed. 2021)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Control, Automation and Robotics
Release date:	September 2020
First published:	2021
Authors:	Xi-Ren Cao
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	120
Edition:	1st ed. 2021
ISBN-13:	978-3-03-056677-7
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Science & Mathematics > Mathematics > Optimization > General Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering > General
LSN:	3-03-056677-3
Barcode:	9783030566777

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