Pub Date : 2024-11-13DOI: 10.1109/TSIPN.2024.3497773
Jiayi Zhang;Guoliang Wei;Derui Ding;Yamei Ju
In this paper, the distributed sequential state estimation problem is addressed for a class of discrete time-varying systems with inaccurate process noise covariance over binary sensor networks. First, with the purpose of reducing communication costs, a special class of sensors called binary sensors, which output only one bit of data, is adopted. The Gaussian tail function is then used to describe the likelihood of the binary measurements. Subsequently, the process noise covariance matrix is modeled as a inverse Wishart distribution. By employing a variational Bayesian approach combined with diffusion filtering strategies, the parameters (i.e., mean and variance) of the prior and posterior probability density functions are formalized for the sequential estimator and the sequential predictor. Then, the fixed-point iteration is utilized to receive the approximate optimal distributions of both system states and estimated covariance matrices. Finally, a simulation example of target tracking demonstrates that our algorithm performs effectively using binary measurement outputs.
{"title":"Distributed Sequential State Estimation Over Binary Sensor Networks With Inaccurate Process Noise Covariance: A Variational Bayesian Framework","authors":"Jiayi Zhang;Guoliang Wei;Derui Ding;Yamei Ju","doi":"10.1109/TSIPN.2024.3497773","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3497773","url":null,"abstract":"In this paper, the distributed sequential state estimation problem is addressed for a class of discrete time-varying systems with inaccurate process noise covariance over binary sensor networks. First, with the purpose of reducing communication costs, a special class of sensors called binary sensors, which output only one bit of data, is adopted. The Gaussian tail function is then used to describe the likelihood of the binary measurements. Subsequently, the process noise covariance matrix is modeled as a inverse Wishart distribution. By employing a variational Bayesian approach combined with diffusion filtering strategies, the parameters (i.e., mean and variance) of the prior and posterior probability density functions are formalized for the sequential estimator and the sequential predictor. Then, the fixed-point iteration is utilized to receive the approximate optimal distributions of both system states and estimated covariance matrices. Finally, a simulation example of target tracking demonstrates that our algorithm performs effectively using binary measurement outputs.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"11 ","pages":"1-10"},"PeriodicalIF":3.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates the distributed estimation problem over networks with highly correlated and noisy inputs. As a first step, this paper proposes an algorithm based on diffusion affine projection Versoria (APV) that can process highly correlated input signals over networks. Following that, the optimal step-size is derived by minimizing the mean-square deviation at each node, so that the tradeoff between convergence rate and steady-state error can be addressed. To reduce estimation bias caused by input noise, two diffusion bias-compensated APV (DBCAPV) algorithms are then developed by solving the asymptotic unbiasedness or local constrained optimization problems. Using the optimal step-size processed through the moving average and reset mechanisms, two variable step-size DBCAPV algorithms are obtained. The simulation results demonstrate that our methods are effective.
{"title":"Variable Step-Size Diffusion Bias-Compensated APV Algorithm Over Networks","authors":"Fuyi Huang;Shuting Yang;Sheng Zhang;Haiqiang Chen;Pengwei Wen","doi":"10.1109/TSIPN.2024.3496255","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3496255","url":null,"abstract":"This paper investigates the distributed estimation problem over networks with highly correlated and noisy inputs. As a first step, this paper proposes an algorithm based on diffusion affine projection Versoria (APV) that can process highly correlated input signals over networks. Following that, the optimal step-size is derived by minimizing the mean-square deviation at each node, so that the tradeoff between convergence rate and steady-state error can be addressed. To reduce estimation bias caused by input noise, two diffusion bias-compensated APV (DBCAPV) algorithms are then developed by solving the asymptotic unbiasedness or local constrained optimization problems. Using the optimal step-size processed through the moving average and reset mechanisms, two variable step-size DBCAPV algorithms are obtained. The simulation results demonstrate that our methods are effective.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"894-904"},"PeriodicalIF":3.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-08DOI: 10.1109/TSIPN.2024.3487422
Daocheng Tang;Ning Pang;Xin Wang
This article investigates the full-state-constrained optimal containment control problem of perturbed nonlinear multiagent systems (MASs). Initially, to balance control accuracy and cost while maintaining the states of MASs within confined regions, an enhanced constrained optimized backstepping (OB) framework is first developed for the multiagent control scenario by adopting an identifier-actor-critic-based reinforcement learning (RL) algorithm, where a novel performance index based on the barrier Lyapunov function (BLF) is integrated into the classic OB framework. Then, to enhance the robustness of the systems, the proposed framework employs disturbance observers to mitigate the effects of unknown external disturbances. Moreover, sufficient conditions are established to ensure that systems maintain stability and expected performance under denial-of-service (DoS) attacks. Subsequently, the controller implements a novel dynamic event-triggered mechanism (DETM), adaptively adjusting the triggering conditions by the estimated neural network (NN) weights in the proposed framework for substantial communication burden reduction. Finally, the stability of the systems is demonstrated using the Lyapunov theory, and a simulation example confirms the feasibility of the proposed scheme.
本文研究了扰动非线性多代理系统（MAS）的全状态约束优化控制问题。首先，为了在将 MAS 的状态保持在受限区域内的同时平衡控制精度和成本，本文针对多代理控制场景，通过采用基于识别器-代理-批判的强化学习（RL）算法，开发了增强型受限优化反步态（OB）框架，并将基于障碍李亚普诺夫函数（BLF）的新型性能指标集成到经典的 OB 框架中。然后，为了增强系统的鲁棒性，所提出的框架采用了干扰观测器来减轻未知外部干扰的影响。此外，还建立了充分条件，以确保系统在拒绝服务（DoS）攻击下保持稳定和预期性能。随后，控制器实施了一种新颖的动态事件触发机制（DETM），通过估计拟议框架中的神经网络（NN）权重自适应地调整触发条件，从而大大减轻了通信负担。最后，利用 Lyapunov 理论证明了系统的稳定性，一个仿真实例证实了所提方案的可行性。
{"title":"Reinforcement Learning-Based Event-Triggered Constrained Containment Control for Perturbed Multiagent Systems","authors":"Daocheng Tang;Ning Pang;Xin Wang","doi":"10.1109/TSIPN.2024.3487422","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3487422","url":null,"abstract":"This article investigates the full-state-constrained optimal containment control problem of perturbed nonlinear multiagent systems (MASs). Initially, to balance control accuracy and cost while maintaining the states of MASs within confined regions, an enhanced constrained optimized backstepping (OB) framework is first developed for the multiagent control scenario by adopting an identifier-actor-critic-based reinforcement learning (RL) algorithm, where a novel performance index based on the barrier Lyapunov function (BLF) is integrated into the classic OB framework. Then, to enhance the robustness of the systems, the proposed framework employs disturbance observers to mitigate the effects of unknown external disturbances. Moreover, sufficient conditions are established to ensure that systems maintain stability and expected performance under denial-of-service (DoS) attacks. Subsequently, the controller implements a novel dynamic event-triggered mechanism (DETM), adaptively adjusting the triggering conditions by the estimated neural network (NN) weights in the proposed framework for substantial communication burden reduction. Finally, the stability of the systems is demonstrated using the Lyapunov theory, and a simulation example confirms the feasibility of the proposed scheme.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"820-832"},"PeriodicalIF":3.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Privacy-preserving consensus can address the information being leaked during distributed computing, encouraging its application in various scenarios. This paper investigates the finite-time privacy-preserving distributed optimal dispatch for energy systems (ESs). Firstly, a dynamic output mask function is designed to ensure that each node's internal state cannot be identified while accomplishing a distributed task. Second, two finite-time privacy-preserving consensus algorithms are presented, including leader–follower and average consensus algorithms. Under the proposed dynamic mask function, the proposed algorithms are local, allowing each node to protect its privacy by adopting the proposed dynamic output mask. The superiority of the proposed algorithm lies in its ability to achieve precise convergence while ensuring privacy protection. Third, the accurate value of the target state can be obtained after finite steps when processing and transmitting information. In addition, several conditions are presented for ensuring the convergence of the algorithms, which is not limited by special topologies such as undirected graphs and balanced graphs. Finally, an application that achieves the distributed optimal dispatch for the CCHP-based (Combined Cooling, Heating, and Power) ESs, and two examples illustrate that the algorithms can be effective access to economic optimization and excellent privacy performance.
隐私保护共识可以解决分布式计算过程中的信息泄露问题，从而促进其在各种场景中的应用。本文研究了能源系统（ES）的有限时间隐私保护分布式优化调度。首先，设计了一个动态输出掩码函数，以确保在完成分布式任务时无法识别每个节点的内部状态。其次，提出了两种有限时间隐私保护共识算法，包括领导者-跟随者共识算法和平均共识算法。在提议的动态掩码函数下，提议的算法是局部的，允许每个节点通过采用提议的动态输出掩码来保护自己的隐私。拟议算法的优越性在于它能在确保隐私保护的同时实现精确收敛。第三，在处理和传输信息时，经过有限的步骤就能获得目标状态的精确值。此外，还提出了确保算法收敛的几个条件，这些条件不受无向图和平衡图等特殊拓扑结构的限制。最后，基于 CCHP（联合供冷、供热和供电）的 ES 实现分布式优化调度的应用和两个示例说明了该算法可以有效实现经济优化和出色的隐私性能。
{"title":"Finite-Time Performance Mask Function-Based Distributed Privacy-Preserving Consensus: Case Study on Optimal Dispatch of Energy System","authors":"Minxue Kong;Feifei Shen;Zhi Li;Xin Peng;Weimin Zhong","doi":"10.1109/TSIPN.2024.3485480","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3485480","url":null,"abstract":"Privacy-preserving consensus can address the information being leaked during distributed computing, encouraging its application in various scenarios. This paper investigates the finite-time privacy-preserving distributed optimal dispatch for energy systems (ESs). Firstly, a dynamic output mask function is designed to ensure that each node's internal state cannot be identified while accomplishing a distributed task. Second, two finite-time privacy-preserving consensus algorithms are presented, including leader–follower and average consensus algorithms. Under the proposed dynamic mask function, the proposed algorithms are local, allowing each node to protect its privacy by adopting the proposed dynamic output mask. The superiority of the proposed algorithm lies in its ability to achieve precise convergence while ensuring privacy protection. Third, the accurate value of the target state can be obtained after finite steps when processing and transmitting information. In addition, several conditions are presented for ensuring the convergence of the algorithms, which is not limited by special topologies such as undirected graphs and balanced graphs. Finally, an application that achieves the distributed optimal dispatch for the CCHP-based (Combined Cooling, Heating, and Power) ESs, and two examples illustrate that the algorithms can be effective access to economic optimization and excellent privacy performance.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"776-787"},"PeriodicalIF":3.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-06DOI: 10.1109/TSIPN.2024.3487411
Bo Liu;Mengjie Hu;Junjie Huang;Qiang Zhang;Yin Chen;Housheng Su
This work studies the discrete-time controllability of a composite network formed by factor networks via Cartesian products. Based on the Popov-Belevitch-Hautus test and properties of Cartesian products, we derive the algebra-theoretic necessary and sufficient conditions for the controllability of the Cartesian product network (CPN), which is devoted to carry out a comprehensive study of the intricate interplay between the node-system dynamics, network topology and the controllability of the CPN, especially the intrinsic connection between the CPN and its factors. This helps us enrich and perfect the theoretical framework of controllability of complex networks, and gives new insight into designing a valid control scheme for larger-scale composite networks.
{"title":"Discrete-Time Controllability of Cartesian Product Networks","authors":"Bo Liu;Mengjie Hu;Junjie Huang;Qiang Zhang;Yin Chen;Housheng Su","doi":"10.1109/TSIPN.2024.3487411","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3487411","url":null,"abstract":"This work studies the discrete-time controllability of a composite network formed by factor networks via Cartesian products. Based on the Popov-Belevitch-Hautus test and properties of Cartesian products, we derive the algebra-theoretic necessary and sufficient conditions for the controllability of the Cartesian product network (CPN), which is devoted to carry out a comprehensive study of the intricate interplay between the node-system dynamics, network topology and the controllability of the CPN, especially the intrinsic connection between the CPN and its factors. This helps us enrich and perfect the theoretical framework of controllability of complex networks, and gives new insight into designing a valid control scheme for larger-scale composite networks.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"868-880"},"PeriodicalIF":3.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-25DOI: 10.1109/TSIPN.2024.3485473
Claudio Battiloro;Lucia Testa;Lorenzo Giusti;Stefania Sardellitti;Paolo Di Lorenzo;Sergio Barbarossa
Graph machine learning methods excel at leveraging pairwise relations present in the data. However, graphs are unable to fully capture the multi-way interactions inherent in many complex systems. An effective way to incorporate them is to model the data on higher-order combinatorial topological spaces, such as Simplicial Complexes (SCs) or Cell Complexes. For this reason, we introduce Generalized Simplicial Attention Neural Networks (GSANs), novel neural network architectures designed to process data living on simplicial complexes using masked self-attentional layers. Hinging on topological signal processing principles, we devise a series of principled self-attention mechanisms able to process data associated with simplices of various order, such as nodes, edges, triangles, and beyond. These schemes learn how to combine data associated with neighbor simplices of consecutive order in a task-oriented fashion, leveraging on the simplicial Dirac operator and its Dirac decomposition. We also prove that GSAN satisfies two fundamental properties: permutation equivariance and simplicial-awareness. Finally, we illustrate how our approach compares favorably with other simplicial and graph models when applied to several (inductive and transductive) tasks, such as trajectory prediction, missing data imputation, graph classification, and simplex prediction.
{"title":"Generalized Simplicial Attention Neural Networks","authors":"Claudio Battiloro;Lucia Testa;Lorenzo Giusti;Stefania Sardellitti;Paolo Di Lorenzo;Sergio Barbarossa","doi":"10.1109/TSIPN.2024.3485473","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3485473","url":null,"abstract":"Graph machine learning methods excel at leveraging pairwise relations present in the data. However, graphs are unable to fully capture the multi-way interactions inherent in many complex systems. An effective way to incorporate them is to model the data on higher-order combinatorial topological spaces, such as Simplicial Complexes (SCs) or Cell Complexes. For this reason, we introduce Generalized Simplicial Attention Neural Networks (GSANs), novel neural network architectures designed to process data living on simplicial complexes using masked self-attentional layers. Hinging on topological signal processing principles, we devise a series of principled self-attention mechanisms able to process data associated with simplices of various order, such as nodes, edges, triangles, and beyond. These schemes learn how to combine data associated with neighbor simplices of consecutive order in a task-oriented fashion, leveraging on the simplicial Dirac operator and its Dirac decomposition. We also prove that GSAN satisfies two fundamental properties: permutation equivariance and simplicial-awareness. Finally, we illustrate how our approach compares favorably with other simplicial and graph models when applied to several (inductive and transductive) tasks, such as trajectory prediction, missing data imputation, graph classification, and simplex prediction.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"833-850"},"PeriodicalIF":3.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1109/TSIPN.2024.3485508
Runhua Wang;Qing Ling;Zhi Tian
This paper investigates the problem of decentralized resource allocation in the presence of Byzantine attacks. Such attacks occur when an unknown number of malicious agents send random or carefully crafted messages to their neighbors, aiming to prevent the honest agents from reaching the optimal resource allocation strategy. We characterize these malicious behaviors with the classical Byzantine attacks model, and propose a class of Byzantine-resilient decentralized resource allocation algorithms augmented with dual-domain defenses. The honest agents receive messages containing the (possibly malicious) dual variables from their neighbors at each iteration, and filter these messages with robust aggregation rules. Theoretically, we prove that the proposed algorithms can converge to neighborhoods of the optimal resource allocation strategy, given that the robust aggregation rules are properly designed. Numerical experiments are conducted to corroborate the theoretical results.
{"title":"Dual-Domain Defenses for Byzantine-Resilient Decentralized Resource Allocation","authors":"Runhua Wang;Qing Ling;Zhi Tian","doi":"10.1109/TSIPN.2024.3485508","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3485508","url":null,"abstract":"This paper investigates the problem of decentralized resource allocation in the presence of Byzantine attacks. Such attacks occur when an unknown number of malicious agents send random or carefully crafted messages to their neighbors, aiming to prevent the honest agents from reaching the optimal resource allocation strategy. We characterize these malicious behaviors with the classical Byzantine attacks model, and propose a class of Byzantine-resilient decentralized resource allocation algorithms augmented with dual-domain defenses. The honest agents receive messages containing the (possibly malicious) dual variables from their neighbors at each iteration, and filter these messages with robust aggregation rules. Theoretically, we prove that the proposed algorithms can converge to neighborhoods of the optimal resource allocation strategy, given that the robust aggregation rules are properly designed. Numerical experiments are conducted to corroborate the theoretical results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"804-819"},"PeriodicalIF":3.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1109/TSIPN.2024.3485532
Xinjue Wang;Esa Ollila;Sergiy A. Vorobyov
Graph Neural Networks (GNNs), particularly Graph Convolutional Neural Networks (GCNNs), have emerged as pivotal instruments in machine learning and signal processing for processing graph-structured data. This paper proposes an analysis framework to investigate the sensitivity of GCNNs to probabilistic graph perturbations, directly impacting the graph shift operator (GSO). Our study establishes tight expected GSO error bounds, which are explicitly linked to the error model parameters, and reveals a linear relationship between GSO perturbations and the resulting output differences at each layer of GCNNs. This linearity demonstrates that a single-layer GCNN maintains stability under graph edge perturbations, provided that the GSO errors remain bounded, regardless of the perturbation scale. For multilayer GCNNs, the dependency of system's output difference on GSO perturbations is shown to be a recursion of linearity. Finally, we exemplify the framework with the Graph Isomorphism Network (GIN) and Simple Graph Convolution Network (SGCN). Experiments validate our theoretical derivations and the effectiveness of our approach.
{"title":"Graph Convolutional Neural Networks Sensitivity Under Probabilistic Error Model","authors":"Xinjue Wang;Esa Ollila;Sergiy A. Vorobyov","doi":"10.1109/TSIPN.2024.3485532","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3485532","url":null,"abstract":"Graph Neural Networks (GNNs), particularly Graph Convolutional Neural Networks (GCNNs), have emerged as pivotal instruments in machine learning and signal processing for processing graph-structured data. This paper proposes an analysis framework to investigate the sensitivity of GCNNs to probabilistic graph perturbations, directly impacting the graph shift operator (GSO). Our study establishes tight expected GSO error bounds, which are explicitly linked to the error model parameters, and reveals a linear relationship between GSO perturbations and the resulting output differences at each layer of GCNNs. This linearity demonstrates that a single-layer GCNN maintains stability under graph edge perturbations, provided that the GSO errors remain bounded, regardless of the perturbation scale. For multilayer GCNNs, the dependency of system's output difference on GSO perturbations is shown to be a recursion of linearity. Finally, we exemplify the framework with the Graph Isomorphism Network (GIN) and Simple Graph Convolution Network (SGCN). Experiments validate our theoretical derivations and the effectiveness of our approach.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"788-803"},"PeriodicalIF":3.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10731637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1109/TSIPN.2024.3485549
Wenbo Zhu;Wenqiang Wu;Qingling Wang
This paper studies the distributed optimization of continuous-time multi-agent systems with time-delay under switching digraphs. An auxiliary system which only requires the information of the number of adjacent agents is first constructed, then a class of new distributed optimization algorithms are proposed. As an application, we extend above algorithms to address distributed economic dispatch issues for smart grids. It is theoretically shown that the new illustrated distributed control strategies can asymptotically realize optimal consensus for multi-agent systems and optimal economic dispatch for smart grids, where the communication time-delay can be nonuniform, and the switching digraphs are uniformly jointly strongly connected. Finally, two simulation examples are provided to validate theoretical results.
{"title":"A Continuous-Time Algorithm for Distributed Optimization With Nonuniform Time-Delay Under Switching and Unbalanced Digraphs","authors":"Wenbo Zhu;Wenqiang Wu;Qingling Wang","doi":"10.1109/TSIPN.2024.3485549","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3485549","url":null,"abstract":"This paper studies the distributed optimization of continuous-time multi-agent systems with time-delay under switching digraphs. An auxiliary system which only requires the information of the number of adjacent agents is first constructed, then a class of new distributed optimization algorithms are proposed. As an application, we extend above algorithms to address distributed economic dispatch issues for smart grids. It is theoretically shown that the new illustrated distributed control strategies can asymptotically realize optimal consensus for multi-agent systems and optimal economic dispatch for smart grids, where the communication time-delay can be nonuniform, and the switching digraphs are uniformly jointly strongly connected. Finally, two simulation examples are provided to validate theoretical results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"765-775"},"PeriodicalIF":3.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-07DOI: 10.1109/TSIPN.2024.3467921
Zhirui Li;Ben K Johnson;Daniel L. Sussman;Carey E. Priebe;Vince Lyzinski
We present a novel approach for finding multiple noisily embedded template graphs in a very large background graph. Our method builds upon the graph-matching-matched-filter technique proposed in Sussman et al. (Sussman, 2020), with the discovery of multiple diverse matchings being achieved by iteratively penalizing a suitable node-pair similarity matrix in the matched filter algorithm. In addition, we propose algorithmic speed-ups that greatly enhance the scalability of our matched-filter approach. We present theoretical justification of our methodology in the setting of correlated Erdős-Rényi graphs, showing its ability to sequentially discover multiple templates under mild model conditions. We additionally demonstrate our method's utility via extensive experiments both using simulated models and real-world datasets, including human brain connectomes and a large transactional knowledge base.
{"title":"Gotta Match 'Em All: Solution Diversification in Graph Matching Matched Filters","authors":"Zhirui Li;Ben K Johnson;Daniel L. Sussman;Carey E. Priebe;Vince Lyzinski","doi":"10.1109/TSIPN.2024.3467921","DOIUrl":"https://doi.org/10.1109/TSIPN.2024.3467921","url":null,"abstract":"We present a novel approach for finding multiple noisily embedded template graphs in a very large background graph. Our method builds upon the graph-matching-matched-filter technique proposed in Sussman et al. (Sussman, 2020), with the discovery of multiple diverse matchings being achieved by iteratively penalizing a suitable node-pair similarity matrix in the matched filter algorithm. In addition, we propose algorithmic speed-ups that greatly enhance the scalability of our matched-filter approach. We present theoretical justification of our methodology in the setting of correlated Erdős-Rényi graphs, showing its ability to sequentially discover multiple templates under mild model conditions. We additionally demonstrate our method's utility via extensive experiments both using simulated models and real-world datasets, including human brain connectomes and a large transactional knowledge base.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"752-764"},"PeriodicalIF":3.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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