We consider the problem of decentralized optimization over time-varying directed networks. The network nodes can access only their local objectives, and aim to collaboratively minimize a global function by exchanging messages with their neighbors. Leveraging sparsification, gradient tracking and variance-reduction, we propose a novel communication-efficient decentralized optimization scheme that is suitable for resource-constrained time-varying directed networks. We prove that in the case of smooth and strongly-convex objective functions, the proposed scheme achieves an accelerated linear convergence rate. To our knowledge, this is the first decentralized optimization framework for time-varying directed networks that achieves such a convergence rate and applies to settings requiring sparsified communication. Experimental results on both synthetic and real datasets verify the theoretical results and demonstrate efficacy of the proposed scheme.
Decentralized design of consensus protocols with minimal communication links based on directed spanning tree
College of Artificial Intelligence and Automation, Beijing University of Technology（北京工业大学人工智能与自动化学院）；Engineering Research Center of Digital Community, Ministry of Education（教育部数字社区工程研究中心）
Provably Accelerated Decentralized Gradient Method Over Unbalanced Directed Graphs
Shanghai Center for Mathematical Sciences, Fudan University（复旦大学上海数学中心）；School of Mathematical Sciences, Fudan University（复旦大学数学科学学院）；Shanghai Key Laboratory for Contemporary Applied Mathematics（上海现代应用数学重点实验室）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen)（香港中文大学（深圳）数据科学学院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）
A Variance-Reduced Stochastic Gradient Tracking Algorithm for Decentralized Optimization with Orthogonality Constraints
State Key Laboratory of Scientific and Engineering Computing, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院科学与工程计算国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Prox-DBRO-VR: A Unified Analysis on Byzantine-Resilient Decentralized Stochastic Composite Optimization with Variance Reduction and Non-Asymptotic Convergence Rates
Central South University（中南大学）；City University of Hong Kong（香港城市大学）；University of New South Wales（新南威尔士大学）；Southwest University（西南大学）
Distributed and Decentralized Optimization Algorithms via Consensus ALADIN
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；University of Pittsburgh（匹兹堡大学）；KTH Royal Institute of Technology（皇家理工学院）；Digital Futures（数字未来）
Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Bandits
E-Surfing Digital Life Technology Co., Ltd.（天翼数字生活科技有限公司）；Stony Brook University（石溪大学）；University of California Santa Cruz（加州大学圣克鲁兹分校）；J.P. Morgan AI Research（摩根大通AI研究院）
A general framework for decentralized optimization with first-order methods
Carnegie Mellon University（卡内基梅隆大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Arizona State University（亚利桑那州立大学）；Tufts University（塔夫茨大学）
2-D Directed Formation Control Based on Bipolar Coordinates
Division of Decision and Control Systems, KTH Royal Institute of Technology（决策与控制系统系，皇家理工学院）；Division of Systems and Control of the Department of Electrical and Computer Engineering at University of Patras（电气与计算机工程系系统与控制系，帕特拉斯大学）；INMA, ICTEAM institute, UCLouvain（INMA，ICTEAM研究所，乌尔特-劳恩大学）
