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Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The book presents a systemati… Read more
SUSTAINABLE DEVELOPMENT
Save up to 30% on top Physical Sciences & Engineering titles!
Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The 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. As there are strong links between optimization and learning, this book provides a unified platform for understanding distributed optimization and learning algorithms for different purposes.
Researchers, industrial practitioners, graduate students in optimization, control engineering, data sciences, machine learning, mechatronics, and applied mathematics, Mathematicians and engineers working on optimisation, learning and control systems, 3rd/4th-year undergraduate students with interests in multi-agent system optimization and control, robotics and machine learning
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