Titlebook: Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems; Tatiana Tatarenko Book 2017 Springer International

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Introduction,Due to the emergence of distributed networked systems, problems of cooperative control in multi-agent systems have gained a lot of attention over the recent years. Some examples of networked multi-agent systems are smart grids, social networks, autonomous vehicle teams, processors in machine learning scenarios, etc.

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Stochastic Methods in Distributed Optimization and Game-Theoretic Learning,This chapter studies the way to apply stochastic approximation procedure, known as the Robbins–Monro procedure [RM51], to . as well as to . and ..

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Conclusion,This book deals with optimization problems and information processing in multi-agent systems through a game-theoretic formulation and distributed learning.

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Book 2017ave the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during communication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different info

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Book 2017zation algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space.?.

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Friedrich Müllerthe boundary integral equation method to new problems.Constr.Many problems in mathematical physics rely heavily on the use of elliptical partial differential equations, and boundary integral methods play a significant role in solving these equations.?.Stationary Oscillations of Elastic Plates. .stud