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distributed dynamic stochastic approximation algorithm over time-varying networks

Autonomous Intelligent Systems 2021年第1期1卷 49-68页

作者： Kewei Fu Han-Fu Chen Wenxiao Zhao Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190People’s Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049People’s Republic of China

In this paper,a distributed stochastic approximation algorithm is proposed to track the dynamic root of a sum of time-varying regression functions over a *** agent updates its estimate by using the local observation,the dynamic information of the global root,and information received from its *** with similar works in optimization area,we allow the observation to be noise-corrupted,and the noise condition is much ***,instead of the upper bound of the estimate error,we present the asymptotic convergence result of the *** consensus and convergence of the estimates are ***,the algorithm is applied to a distributed target tracking problem and the numerical example is presented to demonstrate the performance of the algorithm.

关键词： distributed algorithm Dynamic stochastic approximation algorithm Time-varying network

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distributed Control Design for Solving Linear Algebraic Equations via Adjustable Domains

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第12期69卷 8790-8797页

作者： Li, Juntao Liang, Cong Meng, Deyuan Henan Normal Univ Coll Math & Informat Sci Xinxiang 453007 Peoples R China Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China Beihang Univ BUAA Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

This article aims to develop a general and designable distributed algorithm for solving linear algebraic equations (LAEs), which departs from the design framework based on orthogonal projection. The concept of adjustable domains for the parameter matrix is introduced, enabling the algorithm to derive flexible and variable updating rules for agents. By leveraging adjustable domains in control design, all agents can exponentially converge to a common (least squares) solution of (un)solvable LAEs under arbitrary initialization conditions, regardless of whether the LAEs admit a unique solution or multiple solutions. Moreover, two novel distributed algorithms for obtaining the least squares solution are proposed within both row and column partitioning frameworks. A simulation example is provided to demonstrate the effectiveness of the proposed distributed algorithms.

关键词： distributed algorithms Mathematical models Task analysis Partitioning algorithms Topology Symmetric matrices Matrix decomposition Adjustable domain distributed algorithm linear algebraic equations (LAEs) multiagent system time-varying topology

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A distributed message passing algorithm for computing perfect demand matching

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JOURNAL OF PARALLEL AND distributed COMPUTING 2023年第1期179卷

作者： Dai, Guowei Chen, Yannan Mao, Yaping Xu, Dachuan Zhang, Xiaoyan Zhang, Zan -Bo Nanjing Forestry Univ Coll Sci Nanjing 210037 Peoples R China South China Normal Univ Sch Math Sci Guangzhou 510631 Peoples R China Qinghai Normal Univ Sch Math & Stat Xining 810016 Peoples R China Beijing Univ Technol Dept Operat Res & Informat Engn Beijing 100124 Peoples R China Nanjing Normal Univ Sch Math Sci Nanjing 210023 Peoples R China Guangdong Univ Finance & Econ Sch Stat & Math Guangzhou 510320 Peoples R China Guangdong Univ Finance & Econ Inst Artificial Intelligence & Deep Learning Guangzhou 510320 Peoples R China

In this paper, we consider the perfect demand matching problem (PDM) which combines aspects of the knapsack problem along with the b-matching problem. It is a generalization of the maximum weight matching problem which has been fundamental in the development of theory of computer science and operations research. This problem is NP-hard and there exists a constant c > 0 such that the problem admits no 1 + c-approximation algorithm, unless P=NP. Here, we investigate the performance of a distributed message passing algorithm called Max-sum belief propagation for computing the problem of finding the optimal perfect demand matching. As the main result, we demonstrate the rigorous theoretical analysis of the Max-sum BP algorithm for PDM, and establish that within pseudo-polynomial-time, our algorithm could converge to the optimal solution of PDM, provided that the optimal solution of its LP relaxation is unique and integral. Different from the techniques used in previous literature, our analysis is based on primal-dual complementary slackness conditions, and thus the number of iterations of the algorithm is independent of the structure of the given graph. Moreover, to the best of our knowledge, this is one of a very few instances where BP algorithm is proved correct for NP-hard problems.(c) 2023 Elsevier Inc. All rights reserved.

关键词： distributed algorithm Message passing Belief propagation Perfect demand matching Linear programming dual

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distributed online optimisation in unknown dynamic environment

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2024年第6期55卷 1167-1176页

作者： Wang, Shuang Huang, Bomin Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao Peoples R China Jimei Univ Coll Comp Engn Xiamen Peoples R China Jimei Univ Coll Comp Engn Xiamen 361021 Peoples R China

In this paper, the distributed online optimisation problem is considered in an unknown dynamic environment. Compared with the existing results, an unknown dynamic environment causes the problem to be more challenging. An online optimisation algorithm is designed based on distributed mirror descent and distributed average tracking technology. The analysis of dynamic regret is presented and a bounded regret is obtained. Some simulations are given to verify the validity of the designed algorithm.

关键词： Online optimisation distributed algorithm distributed optimisation unknown dynamic environment

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distributed and latency-aware beaconing for asynchronous duty-cycled IoT networks

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2024年第6期17卷 3650-3668页

作者： Yi, Ming Xie, Qinglin Long, Peng Wu, Yuhang Chen, Quan Zhang, Fanlong Xu, Wenchao Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Beaconing is a fundamental task in IoT networks, where each node tries to locally broadcast a packet to all its neighbors. Unfortunately, the problem of Minimum Latency Beaconing Schedule (MLBS), which tries to obtain the fastest and collision-free schedule, is not well studied when the IoT devices employ the duty-cycled working mode. The existing works have rigid assumptions that there exists a single channel and can only work in a centralized fashion. Aiming at making the work more practical and general, in this paper, we investigate the first distributed method for the MLBS problem in Multi-channel asynchronous Duty-cycled IoT networks (MLBSMD problem). The MLBSMD problem is first formulated and proved to be NP-hard. To avoid collisions locally, several special structures are designed which can work in O(Delta(2)) time, where Delta denotes the maximum node degree in the network. Then, a distributed beaconing scheduling method that can compute a low-latency and collision-free schedule is proposed with a theoretical bound, taking the active time slots of each node into account. Finally, the extensive simulation results demonstrate the effectiveness of the proposed algorithm in terms of latency.

关键词： Beaconing schedule Low latency distributed algorithm Multi-channel Duty-cycled Wireless networks

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A Scalable distributed Link Management Method for Massive IoT With Synchronous Message Passing Neural Network

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第2期12卷 750-762页

作者： Gou, Haosong Du, Pengfei Wang, Xidian Zhang, Gaoyi Zhai, Daosen China Mobile Grp Sichuan Co Ltd Chengdu 610041 Peoples R China Xihua Univ Engn Res Ctr Intelligent Airground Integrated Vehi Minist Educ Chengdu 610039 Peoples R China Yulin Internet Things Collaborat Innovat Res Inst Yulin 719099 Peoples R China

The development of the next generation ubiquitous network has put forward higher requirements for the connection density of communication devices, which has led to a lot of research on link management. However, with the expansion of network scale, the weaknesses of the existing algorithms in computing efficiency, performance, and realizability have become prominent. The emerging graph neural network (GNN) provides a new way to solve this problem. In order to make full use of the broadcast feature of wireless communication, we design a cross-domain distributed GNN structure (named as synchronous message passing neural network (SynMPNN)) combining the measurable index of the actual scene with message passing mechanism. This new GNN structure and the additional input feature dimension (i.e., SINR) work together to provide more comprehensive information for network training. After the initial deployment of the power decision from SynMPNN, we select some links to shut down and others to reduce their transmit power to further improve the system performance and save energy. Simulation results show that our proposed method under distributed execution conditions reaches 83.1% performance of the centralized method. In addition, the discussion on scalability suggests that in order to save training cost, small-scale scenes with the same density can be selected for training in the application of large-scale scenes.

关键词： Training Internet of Things Message passing Graph neural networks Interference Signal to noise ratio Transmitters Scalability Power control Costs Link management graph neural network power control admission control distributed algorithm

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Sign Hessian-Weighted Gradient algorithms for distributed Time-Varying Convex Optimization

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 474-484页

作者： Huang, Bomin Yang, Chengqian Chen, Fei Lan, Weiyao Jimei Univ Coll Comp Engn Xiamen 361021 Peoples R China Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Xiamen Univ Dept Automat Xiamen 361005 Peoples R China

In this article, we initiate our exploration with a time-varying optimization problem, featuring linear equality constraints. To address this complex problem, we introduce a novel continuous-time sign Hessian-weighted gradient algorithm. Remarkably, our proposed approach is capable of handling the time-varying nature of the problem, all without the need for the invertibility of the Hessian matrix associated with the cost function. We present two distinct distributed algorithms: the edge-based and node-based variations. To underscore the practical effectiveness of our methodology, we provide some numerical examples, offering compelling evidence to validate our research findings.

关键词： Cost function Heuristic algorithms Vectors Network systems Time-varying systems distributed algorithms Control systems Convergence Transportation Surges distributed algorithm Hessian matrix sign function time-varying optimization

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distributed Collaborative Feedback Particle Filter: A Diffusion Map-Based Feedback Gain Approximation Approach

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第6期60卷 7611-7627页

作者： Xia, Wei Zhou, Zhuoyang Jiang, Wanyue Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Xinjiang Univ Sch Comp Sci & Technol Sch Cyberspace Secur Urumqi 830046 Peoples R China Geely Automobile Res Inst Co Ltd Chengdu 610095 Peoples R China Nanjing Changjiang Elect Grp Co Ltd Nanjing 210038 Peoples R China

In this article, we consider the diffusion collaborative feedback particle filter (DFPF) for distributed nonlinear target tracking with an adaptive network consisting of geographically-distributed nodes. In the DFPF, each particle at each node is controlled by the collaborative feedback structure incorporating the innovation process and the feedback gain, which is the solution to the constrained Poisson equation. Utilizing the diffusion map (DM) approximation of the exact semigroup of the Poisson equation in the DFPF, we herein develop the feedback gain approximation, the performance of which is pertinent to the Gaussian kernel bandwidth. Further, based on the mean square error induced by the empirical approximation in the DM feedback gain, we derive the adaptive kernel bandwidth under the positive kernel bandwidth constraint. Moreover, we develop the variable integration step-size, referred to as the pseudotime step-size in the DFPF, to efficiently solve the ordinary differential equation, which is utilized to describe the gradual transition from the prior to the posterior. Illustrative simulations validate that the proposed DFPF could achieve enhanced tracking performance with preferable computational efficiency.

关键词： Kernel Bandwidth Target tracking Poisson equations Optimization Mathematical models Collaboration Diffusion strategy distributed algorithm nonlinear filtering particle filtering

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Pade approximant meets federated learning: A nearly lossless, one-shot algorithm for evidence synthesis in distributed research networks with rare outcomes

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JOURNAL OF BIOMEDICAL INFORMATICS 2023年第1期145卷 104476页

作者： Wu, Qiong Schuemie, Martijn J. Suchard, Marc A. Ryan, Patrick Hripcsak, George M. Rohde, Charles A. Chen, Yong Univ Penn Perelman Sch Med Dept Biostat Epidemiol & Informat Philadelphia PA 19104 USA Observat Hlth Data Sci & Informat New York NY USA Janssen Res & Dev Titusville NJ USA Univ Calif Los Angeles Dept Biostat Los Angeles CA USA Univ Calif Los Angeles Dept Human Genet Los Angeles CA USA Columbia Univ Irving Med Ctr Dept Biomed Informat New York NY USA New York Presbyterian Hosp Med Informat Serv New York NY USA Johns Hopkins Univ Dept Biostat Baltimore MD USA

Objective: We developed and evaluated a novel one-shot distributed algorithm for evidence synthesis in distributed research networks with rare *** and Methods: Fed-Pade, motivated by a classic mathematical tool, Pade approximants, reconstructs the multi-site data likelihood via Pade approximant whose key parameters can be computed distributively. Thanks to the simplicity of [2,2] Pade approximant, Fed-Pade requests an extremely simple task and low communication cost for data partners. Specifically, each data partner only needs to compute and share the log-likelihood and its first 4 gradients evaluated at an initial estimator. We evaluated the performance of our algorithm with extensive simulation studies and four observational healthcare ***: Our simulation studies revealed that a [2, 2]-Pade approximant can well reconstruct the multi-site likelihood so that Fed-Pade produces nearly identical estimates to the pooled analysis. Across all simulation scenarios considered, the median of relative bias and rate of instability of our Fed-Pade are both < 0.1%, whereas meta-analysis estimates have bias up to 50% and instability up to 75%. Furthermore, the confidence intervals derived from the Fed-Pade algorithm showed better coverage of the truth than confidence intervals based on the meta-analysis. In real data analysis, the Fed-Pade has a relative bias of < 1% for all three comparisons for risks of acute liver injury and decreased libido, whereas the meta-analysis estimates have a substantially higher bias (around 10%).Conclusion: The Fed-Pade algorithm is nearly lossless, stable, communication-efficient, and easy to implement for models with rare outcomes. It provides an extremely suitable and convenient approach for synthesizing evidence in distributed research networks with rare outcomes.

关键词： distributed algorithm distributed research networks Evidence synthesis Pade approximants Rare outcomes

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Edge-based distributed primal-dual algorithms for seeking generalized Nash equilibria

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EUROPEAN JOURNAL OF CONTROL 2024年 77卷

作者： Li, Huaqing Fan, Luming Ran, Liang Wang, Zheng Zheng, Zuqing Li, Zhe Li, Songyang Zheng, Lifeng Li, Jun Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China

This paper introduces two novel distributed algorithms aimed at finding the generalized Nash equilibria (GNE) in noncooperative games. To tackle this, we utilize the variational inequality framework, transforming the noncooperative game into the challenge of identifying the zeros of a sum of monotone operators. Within noncooperative games, the decisions made by individual players are interlinked via shared affine constraints. Our proposed approach involves two edge -based distributed algorithms, differentiated by their access to players' actions, whether it is full -decision information or partial -decision information. We establish the parameter range and demonstrate the convergence of both algorithms, showcasing their efficacy under local constant step -sizes. Additionally, we validate the effectiveness and advantages of these algorithms through two numerical experiments.

关键词： Primal-dual method distributed algorithm Non-cooperative game Generalized Nash equilibria (GNE)

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