Introduction To Averaging Dynamics Over Networks (lecture Notes In Control And Information Sciences)
by Fabio Fagnani /
2017 / English / PDF
4.9 MB Download
This book deals with averaging dynamics, a paradigmatic example
of network based dynamics in multi-agent systems. The book
presents all the fundamental results on linear averaging
dynamics, proposing a unified and updated viewpoint of many
models and convergence results scattered in the literature.
This book deals with averaging dynamics, a paradigmatic example
of network based dynamics in multi-agent systems. The book
presents all the fundamental results on linear averaging
dynamics, proposing a unified and updated viewpoint of many
models and convergence results scattered in the literature.
Starting from the classical evolution of the powers of a fixed
stochastic matrix, the text then considers more general
evolutions of products of a sequence of stochastic matrices,
either deterministic or randomized. The theory needed for a full
understanding of the models is constructed without assuming any
knowledge of Markov chains or Perron–Frobenius theory. Jointly
with their analysis of the convergence of averaging dynamics, the
authors derive the properties of stochastic matrices. These
properties are related to the topological structure of the
associated graph, which, in the book’s perspective, represents
the communication between agents. Special attention is paid to
how these properties scale as the network grows in size.
Starting from the classical evolution of the powers of a fixed
stochastic matrix, the text then considers more general
evolutions of products of a sequence of stochastic matrices,
either deterministic or randomized. The theory needed for a full
understanding of the models is constructed without assuming any
knowledge of Markov chains or Perron–Frobenius theory. Jointly
with their analysis of the convergence of averaging dynamics, the
authors derive the properties of stochastic matrices. These
properties are related to the topological structure of the
associated graph, which, in the book’s perspective, represents
the communication between agents. Special attention is paid to
how these properties scale as the network grows in size.
Finally, the understanding of stochastic matrices is applied to
the study of other problems in multi-agent coordination:
averaging with stubborn agents and estimation from relative
measurements. The dynamics described in the book find application
in the study of opinion dynamics in social networks, of
information fusion in sensor networks, and of the collective
motion of animal groups and teams of unmanned vehicles.
Finally, the understanding of stochastic matrices is applied to
the study of other problems in multi-agent coordination:
averaging with stubborn agents and estimation from relative
measurements. The dynamics described in the book find application
in the study of opinion dynamics in social networks, of
information fusion in sensor networks, and of the collective
motion of animal groups and teams of unmanned vehicles.Introduction to Averaging Dynamics over Networks
Introduction to Averaging Dynamics over Networks will be
of material interest to researchers in systems and control
studying coordinated or distributed control, networked systems or
multiagent systems and to graduate students pursuing courses in
these areas.
will be
of material interest to researchers in systems and control
studying coordinated or distributed control, networked systems or
multiagent systems and to graduate students pursuing courses in
these areas.
