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Distributed optimization, game and l...

Wang, Huiwei.

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Distributed optimization, game and learning algorithms = theory and applications in smart grid systems /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Distributed optimization, game and learning algorithms/ by Huiwei Wang, Huaqing Li, Bo Zhou.
其他題名:	theory and applications in smart grid systems /
作者:	Wang, Huiwei.
其他作者:	Li, Huaqing.
出版者:	Singapore :Springer Singapore : : 2021.,
面頁冊數:	xvii, 217 p. :ill., digital ;24 cm.
內容註:	Cooperative Distributed Optimization in Multiagent Networks with Delays -- Constrained Consensus of Multi-Agent Systems with Time-Varying Topology -- Distributed Optimization under Inequality Constraints and Random Projections -- Accelerated Distributed Optimization over Digraphs with Stochastic Matrices -- Linear Convergence for Constrained Optimization over Time-Varying Digraphs -- Stochastic Gradient-Push for Economic Dispatch on Time-Varying Digraphs -- Reinforcement Learning in Energy Trading Game Among Smart Microgrids -- Reinforcement Learning for Constrained Games with Incomplete Information -- Reinforcement Learning for PHEV Route Choice based on Congestion Game.
Contained By:	Springer Nature eBook
標題:	Mathematical optimization. -
電子資源:	https://doi.org/10.1007/978-981-33-4528-7
ISBN:	9789813345287

Distributed optimization, game and learning algorithms = theory and applications in smart grid systems /
Wang, Huiwei.

Distributed optimization, game and learning algorithmstheory and applications in smart grid systems /[electronic resource] :by Huiwei Wang, Huaqing Li, Bo Zhou. - Singapore :Springer Singapore :2021. - xvii, 217 p. :ill., digital ;24 cm.

Cooperative Distributed Optimization in Multiagent Networks with Delays -- Constrained Consensus of Multi-Agent Systems with Time-Varying Topology -- Distributed Optimization under Inequality Constraints and Random Projections -- Accelerated Distributed Optimization over Digraphs with Stochastic Matrices -- Linear Convergence for Constrained Optimization over Time-Varying Digraphs -- Stochastic Gradient-Push for Economic Dispatch on Time-Varying Digraphs -- Reinforcement Learning in Energy Trading Game Among Smart Microgrids -- Reinforcement Learning for Constrained Games with Incomplete Information -- Reinforcement Learning for PHEV Route Choice based on Congestion Game.

This book provides the fundamental theory of distributed optimization, game and learning. It includes those working directly in optimization,-and also many other issues like time-varying topology, communication delay, equality or inequality constraints,-and random projections. This book is meant for the researcher and engineer who uses distributed optimization, game and learning theory in fields like dynamic economic dispatch, demand response management and PHEV routing of smart grids.

ISBN: 9789813345287

Standard No.: 10.1007/978-981-33-4528-7doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: TK7881.15 / .W364 2021

Dewey Class. No.: 519.6

Distributed optimization, game and learning algorithms = theory and applications in smart grid systems /
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505 0 $a Cooperative Distributed Optimization in Multiagent Networks with Delays -- Constrained Consensus of Multi-Agent Systems with Time-Varying Topology -- Distributed Optimization under Inequality Constraints and Random Projections -- Accelerated Distributed Optimization over Digraphs with Stochastic Matrices -- Linear Convergence for Constrained Optimization over Time-Varying Digraphs -- Stochastic Gradient-Push for Economic Dispatch on Time-Varying Digraphs -- Reinforcement Learning in Energy Trading Game Among Smart Microgrids -- Reinforcement Learning for Constrained Games with Incomplete Information -- Reinforcement Learning for PHEV Route Choice based on Congestion Game.
520 $a This book provides the fundamental theory of distributed optimization, game and learning. It includes those working directly in optimization,-and also many other issues like time-varying topology, communication delay, equality or inequality constraints,-and random projections. This book is meant for the researcher and engineer who uses distributed optimization, game and learning theory in fields like dynamic economic dispatch, demand response management and PHEV routing of smart grids.
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