Distributed Optimization, Game and Learning Algorithms

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.

Is the first professional book on the fusion of the distributed optimization, game, and learning theory and applications in smart grids Introduces the latest distributed optimization, game, and learning technology accompanied by strict step-by-step theoretical analysis, providing comprehensive guidance for professional researchers and college students Proposes the efficient, universal algorithms verified by the benchmark smart grid systems, and numerous application examples, which can help the reader to learn quickly

Autorentext

Huiwei WANG received his Ph.D. in Computer Science from Chongqing University, China, in 2014. Now, he is Associate Professor in Southwest University, China. He was Postdoctoral Research Associate with Texas A&M University at Qatar, from 2014 to 2016, and Research Fellow with the University of New South Wales, Australia, from 2019 to 2020.

Huaqing LI received his Ph.D. in Computer Science from Chongqing University, China, in 2013. Now, he is Professor in Southwest University, China. He was Postdoctoral Research Associate with the University of Sydney, Australia, from 2014 to 2015, and Research Fellow with Nanyang Technological University, Singapore from, 2015 to 2016.

Bo ZHOU received his Ph.D. in Applied Mathematics from Southwest University, China, in 2016. Now, he is Associate professor in Chongqing Jiaotong University, China.

Inhalt
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.