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Convergence Rate of Distributed Averaging Dynamics and Optimization in Networks (Paperback)
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Convergence Rate of Distributed Averaging Dynamics and Optimization in Networks (Paperback)
Series: Foundations and Trends (R) in Systems and Control
Expected to ship within 10 - 15 working days
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Recent years have seen the advent of new large cyber-physical
systems such as sensor and social networks. These network systems
are typically spatially distributed over a large area and may
consists of hundreds of agents in smart-sensor networks to millions
of agents in social networks. As such, they do not possess a
central coordinator or a central point for access to the complete
system information. This lack of central entity makes the
traditional (centralized) optimization and control techniques
inapplicable, thus necessitating the development of new distributed
computational models and algorithms to support efficient operations
over such networks. This tutorial provides an overview of the
convergence rate of distributed algorithms for coordination and its
relevance to optimization in a system of autonomous agents embedded
in a communication network, where each agent is aware of (and can
communicate with) its local neighbors only. The focus is on
distributed averaging dynamics for consensus problems and its role
in consensus-based gradient methods for convex optimization
problems, where the network objective function is separable across
the constituent agents. It will be of interest to researchers and
engineers working on a wide-variety of operations research,
networking and optimization problems.
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