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Book synopsis

Decentralized algorithms are useful for solving large-scale complex optimization problems, which not only alleviate the single-point resource bottleneck problem of centralized algorithms, but also possess higher scalability. Decentralized Optimization in Networks: Algorithmic Efficiency and Privacy Preservation provides the reader with theoretical foundations, practical guidance, and problem-solving approaches to decentralized optimization. It teaches how to apply decentralized optimization algorithms to improve optimization efficiency (communication efficiency, computational efficiency, fast convergence), solve large-scale problems (training for large-scale datasets), achieve privacy preservation (effectively counter external eavesdropping attacks, differential attacks, etc), and overcome a range of challenges in complex decentralized network environments (random sleep, random link failures, time-varying, directed, etc). It focuses on: 1) communication-efficiency: event-triggered communication, random link failures, zeroth-order gradients. 2) computation-efficiency: variance-reduction, Polyak’s projection, stochastic gradient, random sleep. 3) privacy preservation: differential privacy, edge-based correlated perturbations, conditional noises. It uses simulation results, including practical application examples, to illustrate the effectiveness and the practicability of decentralized optimization algorithms.

• Introduces the latest and advanced algorithms in decentralized optimization of networked control systems
• Proposes effective strategies for efficient execution and privacy preservation in the development of decentralized optimization algorithms
• Constructs the frameworks of convergence and complexity analysis, privacy and security proof, and performance evaluation
• Includes systematic detailed implementations on how decentralized optimization algorithms solve the problems in real world systems: smart grid systems, online learning systems, wireless sensor systems, etc
• Helps reader to develop their own novel decentralized optimization algorithms

Book details

Book category Books in English Computing & information technology Operating systems

• Full title: Decentralized Optimization in Networks
• Subtitle: Algorithmic Efficiency and Privacy Preservation
• Author: Qingguo Lu, Xiaofeng Liao, Huaqing Li, Shaojiang Deng, Yantao Li, Keke Zhang
• Language: English
• Binding: Paperback
• Number of pages: 300
• EAN: 9780443333378
• ISBN: 0443333378
• ID: 46143231
• Publisher: Elsevier Science
• Weight: 450 g
• Dimensions: 235 × 191 mm
• Date of publishing: 01. September 2025

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