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Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains/ by Xi-Ren Cao.
作者:	Cao, Xi-Ren.
面頁冊數:	VIII, 120 p. 36 illus., 3 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Optimization. -
電子資源:	https://doi.org/10.1007/978-3-030-56678-4
ISBN:	9783030566784

Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains
Cao, Xi-Ren.

Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains[electronic resource] /by Xi-Ren Cao. - 1st ed. 2021. - VIII, 120 p. 36 illus., 3 illus. in color.online resource. - SpringerBriefs in Control, Automation and Robotics,2192-6794. - SpringerBriefs in Control, Automation and Robotics,.

Chapter 1. Introduction -- Chapter 2. Confluencity and State Classification -- Chapter 3. Optimization of Average Rewards and Bias: Single Class -- Chapter 4. Optimization of Average Rewards: Multi-Chains -- Chapter 5. The Nth-Bias and Blackwell Optimality.

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.

ISBN: 9783030566784

Standard No.: 10.1007/978-3-030-56678-4doiSubjects--Topical Terms:

669174
Optimization.

LC Class. No.: TJ212-225

Dewey Class. No.: 629.8

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