Distributed Optimization, Game and Learning Algorithms

Book Description 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.

Content

1. Cooperative Distributed Optimization in Multiagent Networks with Delays
2. Constrained Consensus of Multi-agent Systems with Time-Varying Topology
3. Distributed Optimization Under Inequality Constraints and Random Projections
4. Accelerated Distributed Optimization over Digraphs with Stochastic Matrices
5. Linear Convergence for Constrained Optimization Over Time-Varying Digraphs
6. Stochastic Gradient-Push for Economic Dispatch on Time-Varying Digraphs
7. Reinforcement Learning in Energy Trading Game Among Smart Microgrids
8. Reinforcement Learning for Constrained Games with Incomplete Information
9. Reinforcement Learning for PHEV Route Choice Based on Congestion Game