Decentralized Optimization in Networks

Decentralized Optimization in Networks

Architect the distributed intelligence of the next era and master the high-performance protocols of modern networked systems. Decentralized Optimization in Networks provides a definitive, mathematical-first roadmap to the most significant shift in computational logic since the invention of the cloud. Learn how to move beyond centralized bottlenecks to high-velocity, multi-agent coordination—bridging the gap between a standalone node and a sophisticated, self-organizing network—ensuring your technical infrastructures are resilient, scalable, and ready for the 2026 global data landscape.

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What You Will Learn:

Foundations of Distributed Algorithms: Master the core principles of consensus-based optimization, subgradient methods, and the essential mechanics of peer-to-peer data processing.

Modern Multi-Agent Coordination: Step-by-step guidance on utilizing collaborative filtering and task allocation to maintain the structural integrity of complex, large-scale networks.

Optimization Under Constraints: Discover how to utilize stochastic gradients and projection methods to solve real-time resource management problems with professional-grade precision.

Strategic Security & Byzantine Resilience: Learn advanced techniques for maintaining information security within decentralized topologies, protecting the network against malicious nodes and data manipulation.

Who This Book is For: This professional-grade guide is essential for Network Engineers, Research Scientists, and Control Theory Specialists. It is an invaluable resource for any technical lead—including those building highly secure, distributed graduation projects like Smart Guard—aiming to master the technical agility and structural precision required for modern, decentralized software delivery.

Product Details:

Format: Digital PDF Download

Authors: Qingguo Lü; Xiaofeng Liao; Huaqing Li; Shaojiang Deng; Yantao Li; Keke Zhang

Publisher: Elsevier (Academic Press)

ISBN-13: 9780443333385

ISBN-10: 0443333386