Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains

Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains

English | ISBN: 3030566773 | 2021 | 128 Pages | PDF | 2 MB

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.


[Fast Download] Foundations of Average-Cost Nonhomogeneous Controlled Markov Chains

Related eBooks:
Employment Law: A Practical Introduction (HR Fundamentals), 2nd Edition
Teach Yourself About Shares, 3rd Edition
City Planning
Outlooks: Lesbian and Gay Sexualities and Visual Cultures
Advanced Macroeconomics, 5th Edition
Slow Down: 50 Mindful Moments in Nature
One Simple Idea for Startups and Entrepreneurs
Elementary Probability with Applications, 2nd Edition
Schaum's Outline of Mathematical Handbook of Formulas and Tables, 5th Edition
Better Than Perfect: How Gifted Bosses and Great Employees Can Lift the Performance of Those Around
Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysi
Painless Geometry (Painless)
Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.