Product of Random Stochastic Matrices and Distributed Averaging [electronic resource] /
Touri, Behrouz.author.
The thesis deals with averaging dynamics in a multiagent networked system, which is a main mechanism for diffusing the information over such networks. It arises in a wide range of applications in engineered physical networks (such as mobile communication and sensor networks), as well as social and economic networks. The thesis provides in depth study of  stability and other phenomena characterizing the limiting behavior of both deterministic and random averaging dynamics. By developing new concepts, and using the tools from dynamic system theory and non-negative matrix theory, several novel fundamental results are rigorously developed. These contribute significantly to our understanding of averaging dynamics as well as to non-negative random matrix theory. The exposition, although highly rigorous and technical, is elegant and insightful, and accompanied with numerous illustrative examples, which makes this thesis work easily accessible to those just entering this field and will also be much appreciated by experts in the field.
Description based upon print version of record.
Doctoral thesis accepted by the University of Illinois at Urbana-Champaign, IL, USA.
Includes bibliographical references and index.
Introduction -- Products of Stochastic Matrices and Averaging Dynamics -- Ergodicity of Random Chains -- Infinite Flow Stability -- Implications -- Absolute Infinite Flow Property -- Averaging Dynamics in General State Spaces -- Conclusion and Suggestions for Future Works -- Appendices.
The thesis deals with averaging dynamics in a multiagent networked system, which is a main mechanism for diffusing the information over such networks. It arises in a wide range of applications in engineered physical networks (such as mobile communication and sensor networks), as well as social and economic networks. The thesis provides in depth study of  stability and other phenomena characterizing the limiting behavior of both deterministic and random averaging dynamics. By developing new concepts, and using the tools from dynamic system theory and non-negative matrix theory, several novel fundamental results are rigorously developed. These contribute significantly to our understanding of averaging dynamics as well as to non-negative random matrix theory. The exposition, although highly rigorous and technical, is elegant and insightful, and accompanied with numerous illustrative examples, which makes this thesis work easily accessible to those just entering this field and will also be much appreciated by experts in the field.
Control engineering.
Statistical physics.
Dynamical systems.
Probabilities.
Mathematical statistics.
Statistics .
Control and Systems Theory.
Complex Systems.
Probability Theory and Stochastic Processes.
Probability and Statistics in Computer Science.
Statistical Theory and Methods.
Statistical Physics and Dynamical Systems.
Springer Theses