Distributed Optimization by Network Flows With Spatio-Temporal Compression

• 成果类型: Article
• 署名作者: Ren, Zihao; Wang, Lei; Yi, Xinlei; Wang, Xi; Yuan, Deming; Yang, Tao; Wu, Zhengguang; Shi, Guodong
• 署名单位: Zhejiang University; Tongji University; University of New South Wales Sydney; Nanjing University of Science & Technology; Northeastern University - China; University of Sydney
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2026.3654479
• 发表日期: 2026
• 关键词: convex-optimization; consensus; COMMUNICATION; algorithms; COORDINATION

摘要

Several data compressors have been proposed in distributed optimization frameworks of network systems to reduce communication overhead in large-scale applications. In this article, we demonstrate that effective information compression may occur over time or space during sequences of node communications in distributed algorithms, leading to the concept of spatio-temporal compressors. This abstraction classifies existing compressors and inspires new compressors as spatio-temporal compressors, with their effectiveness described by constructive stability criteria from nonlinear system theory. Subsequently, we incorporate these spatio-temporal compressors directly into standard continuous-time consensus flows and distributed primal-dual flows, establishing conditions ensuring exponential convergence. In addition, we introduce a novel observer-based distributed primal-dual continuous flow integrated with spatio-temporal compressors, which provides broader convergence conditions. These continuous flows achieve exponential convergence to the global optimum when the objective function is strongly convex and can be discretized using Euler approximations. Finally, numerical simulations illustrate the versatility of the proposed spatio-temporal compressors and verify the convergence of algorithms.