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Asymptotic convergence of a distributed weighted least squares algorithm for networked systems with vector node variables

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SYSTEMS & CONTROL LETTERS 2022年 165卷

作者： Yang, Qiqi Zhang, Zhaorong Fu, Minyue Cai, Qianqian Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Univ Newcastle Sch Elect Engn & Comp Callaghan NSW 2308 Australia Guangdong Univ Technol Sch Automation Guangzhou 510006 Peoples R China

This paper studies the convergence properties of a recently proposed distributed algorithm for weighted least-squares (WLS) estimation in networked systems. This algorithm is suitable for largescale networks with a vector parameter (variable) in each node of the network. By establishing the connection between this algorithm and the celebrated Gaussian Belief Propagation (BP) algorithm for statistical learning with scalar variables, asymptotic convergence of the algorithm is established under the assumption of generalised block diagonal dominance. This result generalises the known asymptotic convergence result of the Gaussian BP algorithm for networks with scalar variables. By extending the notion of diagonal dominance to block matrices, we are able to generalise the so-called walk-sum approach for convergence analysis of the Gaussian BP algorithm to this distributed WLS algorithm and show a similar asymptotic convergence property for networks with vector parameters. The significance of our work is that it gives theoretical guarantee for the distributed WLS algorithm for a new class of large-scale networked systems with vector parameters. (C) 2022 Published by Elsevier B.V.

关键词： distributed algorithm distributed optimisation distributed estimation Gaussian belief propagation Message passing

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Fully cooperative and distributed focal underdetermined system solver compressive sensing recovery algorithm for wireless sensor networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2022年第9期35卷 e5126-e5126页

作者： Azarnia, Ghanbar Sharifi, Abbas Ali Urmia Univ Technol Engn Fac Khoy Orumiyeh Iran Univ Bonab Dept Elect Engn Bonab Iran

The designed algorithms for wireless sensor networks (WSNs) should not impose a high computational load on sensor nodes because they are battery-powered devices. To distribute the computations between sensors, distributed algorithms are required. On the other hand, since the sensors observe related phenomena, the acquired signals, besides the intra-signal correlation, also have some inter-signal correlation. When the designed algorithms are cooperative, these joint structures are used. In this study, we propose a distributed and cooperative algorithm based on the focal underdetermined system solver (FOCUSS) for compressive sensing signal reconstruction in WSNs. Unlike the other FOCUSS-based sparse recovery algorithms, the proposed algorithm is quite distributed and implemented by one-hop communication between the neighboring node without collaborating any fusion center (FC). In the suggested system model, the reconstructed signals from the underdetermined systems of equations are different for each sensor node. The obtained results verified that the proposed approach performs better than the existing works regarding signal reconstruction and convergence rate.

关键词： compressive sensing distributed algorithm sparse signals wireless sensor networks

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Optimal distributed Model Averaging for Multivariate Additive Model

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JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY 2025年 1-25页

作者： Song, Minghui Qu, Tianyao Zhao, Zhihao Zou, Guohua Capital Normal Univ Sch Math Sci Beijing 100048 Peoples R China Capital Univ Econ & Business Sch Stat Beijing 100070 Peoples R China

In the era of massive data, the study of distributed data is a significant topic. Model averaging can be effectively applied to distributed data by combining information from all machines. For linear models, the model averaging approach has been developed in the context of distributed data. However, further investigation is needed for more complex models. In this paper, the authors propose a distributed optimal model averaging approach based on multivariate additive models, which approximates unknown functions using B-splines allowing each machine to have a different smoothing degree. To utilize the information from the covariance matrix of dependent errors in multivariate multiple regressions, the authors use the Mahalanobis distance to construct a Mallows-type weight choice criterion. The criterion can be computed by transmitting information between the local machines and the center machine in two steps. The authors demonstrate the asymptotic optimality of the proposed model averaging estimator when the covariates are subject to uncertainty, and obtain the convergence rate of the weight vector to the theoretically optimal weights. The results remain novel even for additive models with a single response variable. The numerical examples show that the proposed method yields good performance.

关键词： Additive model asymptotic optimality consistency distributed algorithm weight choice

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distributed Quasi-Poisson regression algorithm for modeling multi-site count outcomes in distributed data networks

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JOURNAL OF BIOMEDICAL INFORMATICS 2022年第0期131卷 104097页

作者： Edmondson, Mackenzie J. Luo, Chongliang Islam, Md. Nazmul Sheils, Natalie E. Buresh, John Chen, Zhaoyi Bian, Jiang Chen, Yong Univ Penn Perelman Sch Med Dept Biostat Epidemiol & Informatics Philadelphia PA 19104 USA UnitedHlth Grp Optum Labs Minnetonka MN USA Univ Florida Coll Med Dept Hlth Outcomes & Biomed Informat Gainesville FL USA Univ Florida Hlth Sci Ctr Canc Informat Shared Resource Gainesville FL USA

Background: Observational studies incorporating real-world data from multiple institutions facilitate study of rare outcomes or exposures and improve generalizability of results. Due to privacy concerns surrounding patient-level data sharing across institutions, methods for performing regression analyses distributively are desirable. Meta-analysis of institution-specific estimates is commonly used, but has been shown to produce biased estimates in certain settings. While distributed regression methods are increasingly available, methods for analyzing count outcomes are currently limited. Count data in practice are commonly subject to overdispersion, exhibiting greater variability than expected under a given statistical model. Objective: We propose a novel computational method, a one-shot distributed algorithm for quasi-Poisson regression (ODAP), to distributively model count outcomes while accounting for overdispersion. Methods: ODAP incorporates a surrogate likelihood approach to perform distributed quasi-Poisson regression without requiring patient-level data sharing, only requiring sharing of aggregate data from each participating institution. ODAP requires at most three rounds of non-iterative communication among institutions to generate coefficient estimates and corresponding standard errors. In simulations, we evaluate ODAP under several data scenarios possible in multi-site analyses, comparing ODAP and meta-analysis estimates in terms of error relative to pooled regression estimates, considered the gold standard. In a proof-of-concept real-world data analysis, we similarly compare ODAP and meta-analysis in terms of relative error to pooled estimatation using data from the OneFlorida Clinical Research Consortium, modeling length of stay in COVID-19 patients as a function of various patient characteristics. In a second proof-of-concept analysis, using the same outcome and covariates, we incorporate data from the UnitedHealth Group Clinical Discovery Database to

关键词： distributed algorithm distributed data network Electronic health records Overdispersion Poisson regression

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distributed Latency-Efficient Beaconing for Multi-channel Asynchronous Duty-Cycled IoT Networks 1

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23rd International Conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Long, Peng Wu, Yuhang Chen, Quan Cheng, Lianglun Tao, Yongchao Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Peoples R China Shenzhen Acad Aerosp Technol Shenzhen Peoples R China

ISBN: (数字)9789819708086

ISBN: (纸本)9789819708079;9789819708086

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 a 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 in networks, 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 firstly formulated and proved to be NP-hard. To avoid the collisions locally, several special structures are designed, which works in O(Delta) time, where Delta denotes the maximum node degree in the network. Then, a distributed beaconing scheduling method which can compute a low-latency and collision-free schedule is proposed with a theoretical bounded, 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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Recovery of Time-Varying Graph Signals via distributed algorithms on Regularized Problems

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2020年 6卷 540-555页

作者： Jiang, Junzheng Tay, David B. Sun, Qiyu Ouyang, Shan Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China Deakin Univ Sch Informat Technol Waurn Ponds Vic 3216 Australia Univ Cent Florida Dept Math Orlando FL 32816 USA

The recovery of missing samples from available noisy measurements is a fundamental problem in signal processing. This process is also sometimes known as graph signal inpainting, reconstruction, forecasting or inference. Many of the existing algorithms do not scale well with the size of the graph and/or they cannot be implemented efficiently in a distributed manner. In this paper, we develop efficient distributed algorithms for the recovery of time-varying graph signals. The a priori assumptions are that the signal is smooth with respect to the graph topology and correlative across time. These assumptions can be incorporated in an optimization formulation of the algorithm via Tikhonov regularization terms. Our formulation is tailored to yield algorithms that can be efficiently implemented in a distributed manner. Two different distributed algorithms, arising from two different formulations, are proposed to solve the optimization problems. The first involves the l(2)-norm, and a distributed least squared recovery algorithm (DLSRA) is proposed that leverages the graph topology and sparsity of the corresponding Hessian matrix. Updates of the Hessian inverse are not required here. The second involves the l(1)-norm and the philosophy of the alternating direction method of multipliers (ADMM) is utilized to develop the algorithm. An inexact Newton method is incorporated into the conventional ADMM to give a distributed ADMM recovery algorithm (DAMRA). The two distributed algorithms require only data exchanges between vertices in localized neighbourhood subgraphs. Experiments on a variety of synthetic and real-world datasets demonstrate that the proposed algorithms are superior to the existing methods in terms of the computational complexity and convergence rate.

关键词： distributed algorithms Signal processing algorithms Topology Optimization Signal processing Convex functions Convergence Graph signal recovery distributed algorithm least squares method alternative direction method of multipliers (ADMM)

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Online log parsing using evolving research tree

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KNOWLEDGE AND INFORMATION SYSTEMS 2024年第2期66卷 1231-1255页

作者： Vervaet, Arthur Callau-Zori, Mar Chabchoub, Yousra Chiky, Raja 3DS OUTSCALE F-92210 St Cloud France ISEP Inst Super Elect Paris F-92130 Issy Les Moulineaux France

Logs are a reliable source of information for development and maintenance purposes. They record information at runtime regarding the state of a system and are commonly used to analyze its behavior. Parsing operations on logs structure the information embedded within the log message and are a crucial step for many log mining applications. In such use cases, parsing effectiveness can impact performance. For systems that require real-time performance, parsing efficiency is also an important factor. In this paper, we present USTEP, an online log parser that uses an evolving tree structure to encode and discover new parsing rules on the fly. Our evaluation of 14 datasets from different logging environments highlights the superiority of our method in terms of robustness and effectiveness compared to the state of the art. Our analysis of space and time complexity shows that USTEP is the only considered method capable of processing logs in constant time regardless of their length. We also propose here USTEP-UP, a way of running multiple USTEP instances in parallel.

关键词： Log parsing Online algorithm distributed algorithm Log mining

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Economic Dispatch Strategy for Virtual Power Plants Based on distributed Multi-Agent algorithms 3

Economic Dispatch Strategy for Virtual Power Plants Based on...

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3rd International Conference on Energy and Electrical Power Systems (ICEEPS)

作者： Jiang, Dong Wang, Chenxia Xue, Yali Tsinghua Univ Dept Energy & Power Engn State Key Lab Power Syst Operat & Control Beijing Peoples R China Harbin Inst Technol Weihai Coll New Energy Weihai Peoples R China Tsinghua Univ Tsinghua Shanxi Clean Energy Res Inst State Key Lab Power Syst Operat & Control Dept Energy & Power Engn Beijing Peoples R China

ISBN: (纸本)9798350375145;9798350375138

This paper proposes an economic dispatch strategy for virtual power plants(VPP) based on distributed multi-agent algorithms to optimize resource allocation and maximize the VPP economy, in response to the volatility and uncertainty associated with a significant amount of renewable energy. An economic dispatch model for VPP is established in which the coefficients of the cost function are time-varying to account for the effect of the renewable energy output variation. Then a distributed multi-agent algorithm based on graph theory and the Lagrange multiplier method is developed to facilitate communication among individual units and minimize the total operational cost of the system. The effectiveness of the proposed algorithm is validated through numerical examples, which demonstrate its capability to optimize resource allocation, achieve economic dispatch for the VPP, and exhibit a rapid rate of convergence.

关键词： component distributed algorithm multi-agent VPP economic dispatch

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distributed Inexact Nash Equilibrium Seeking for Aggregative Game with Stripe Noises 43

Distributed Inexact Nash Equilibrium Seeking for Aggregative...

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43rd Chinese Control Conference (CCC)

作者： Song, Xiaobo Wang, Yinghui Hu, Yanpeng Liu, Yan Univ Sci & Technol Beijing Key Lab Knowledge Automat Ind Proc Minist Educ Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

In this paper, we consider a specific type of classical noncooperative game model, known as the aggregative game, which takes in to account a time-varying topology under the influence of stochastic stripe noises. In order to seek the Nash equilibrium of the problem, a first-order distributed optimization algorithm is proposed based on the gradient descent method and consensus mechanism. Each player involved in this game aims to minimize its cost function under global convex constraints. Next, the convergence to an inexact Nash equilibrium of the proposed algorithm is confirmed. Ultimately, some numerical results are presented to demonstrate the effectiveness of the proposed algorithm in practical application scenarios.

关键词： distributed algorithm Multi-Agent Systems Nash Equilibrium Aggregative Game Stripe Noise

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A Hierarchical OPF algorithm With Improved Gradient Evaluation in Three-Phase Networks

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 825-837页

作者： Liang, Heng Zhou, Xinyang Zhao, Changhong Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China Natl Renewable Energy Lab Golden CO 80401 USA

Linear approximation commonly used in solving ac optimal power flow (OPF) simplifies the system models but incurs accumulated voltage errors in large power networks. Such errors will make the primal-dual type gradient algorithms converge to solutions with voltage violations. In this article, we improve a recent hierarchical OPF algorithm that rested on primal-dual gradients evaluated with a linearized distribution power flow model. Specifically, we propose a more accurate gradient evaluation method based on an unbalanced three-phase nonlinear distribution power flow model to mitigate the errors arising from linearization. The resultant gradients feature a blocked structure that enables our development of an improved hierarchical primal-dual algorithm to solve the OPF problem. Numerical results on the IEEE 123-bus test feeder and a 4518-node test feeder show that the proposed method can enhance voltage safety at comparable computational efficiency with the linearized algorithm.

关键词： Load flow Approximation algorithms Vectors Network systems Control systems Computational modeling Voltage control distributed algorithm optimal power flow (OPF) unbalanced three-phase networks voltage control

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