Decentralized Optimization in Networks

Decentralized Optimization in Networks by Qingguo Lü, Xiaofeng Liao, Huaqing Li, Shaojiang Deng, Yantao Li, and Keke Zhang is a practical, research-driven guide that brings clarity to the fast-evolving field of distributed optimization for networks. Whether you work in academia, industry, or smart-city development, this book captures the techniques and theory needed to design scalable, robust algorithms across wireless, sensor, and industrial IoT networks.

Begin with a clear overview of decentralized optimization principles and why they matter for modern networked systems: privacy-preserving learning, reduced communication cost, and resilience to node failures. The authors synthesize rigorous analysis and real-world concerns, covering consensus methods, gradient-tracking, ADMM variants, primal–dual schemes, asynchronous updates, and convergence rate guarantees under realistic communication constraints.

Readable derivations and illustrative examples make complex mathematics accessible, while case studies tie methods to applications in distributed machine learning, energy management in smart grids, and multi-agent coordination for urban infrastructures. Emphasis on scalability, network topology effects, and implementation trade-offs helps engineers make practical design choices.

This edition is tailored for graduate students, researchers, and system architects who need both theoretical foundations and actionable insight. If you’re building decentralized algorithms for sensor networks, edge computing, or collaborative robotics, this book equips you to move from concept to deployment with confidence.

Order your copy to deepen your mastery of decentralized optimization and unlock robust, efficient solutions for networked systems worldwide.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.