Distributed Optimisation and Learning

Distributed Optimisation and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. This book presents a systematic and self-contained description of distributed optimization and learning algorithms from a control-theoretic perspective. It focuses on exploring control-theoretic approaches and how those approaches can be utilized to solve distributed optimization and learning problems over network-connected multi-agent systems. It is observed that there are strong links between optimization and learning, and this book intends provides a unified platform for understanding and applicability of distributed optimization and learning algorithms for different purposes. Provides a series of the latest results in, including but not limited to, distributed cooperative and competitive optimization, machine learning, and optimal resource allocation Presents the most recent advances in theory and applications of distributed optimization and machine learning with insightful connections to traditional control techniques Offers numerical and simulation results in each chapter in order to reflect the engineering practice, and demonstrate the main focus of the developed analysis and synthesis approaches