Distributed Optimization and Learning
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Distributed Optimization 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.
Erscheint im August