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.

Second-Order Consensus of Continuous-Time Multi-Agent Systems focuses on the characteristics and features of second-order agents, communication networks, and control protocols/algorithms in continuous consensus of multi-agent systems. The book provides readers with background on consensus control of multi-agent systems and introduces the intrinsic characteristics of second-order agents' behavior, including the development of continuous control protocols/algorithms over various types of underlying communication networks, as well as the implementation of computation- and communication-efficient strategies in the execution of protocols/algorithms. The book's authors also provide coverage of the frameworks of stability analysis, algebraic criteria and performance evaluation. On this basis, the book provides an in-depth study of intrinsic nonlinear dynamics from agents' perspective, coverage of unbalanced directed topology, random switching topology, event-triggered communication, and random link failure, from a communication networks' perspective, as well as leader-following control, finite-time control, and global consensus control, from a protocols/algorithms' perspective. Finally, simulation results including practical application examples are presented to illustrate the effectiveness and the practicability of the control protocols and algorithms proposed in this book.

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.