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distributed fractional local ratio and independent set approximation

INFORMATION AND COMPUTATION 2025年 303卷

作者： Halldorsson, Magnus M. Rawitz, Dror Reykjavik Univ Dept Comp Sci Reykjavik Iceland Bar Ilan Univ Fac Engn Ramat Gan Israel

We consider the MAXIMUM WEIGHT INDEPENDENT SET problem, with a focus on obtaining good approximations for graphs of small maximum degree A. We give deterministic local algorithms running in time poly(A, log n ) that come close to matching the best centralized results known and improve the previous distributed approximations by a factor of about 2. More precisely, we obtain approximations below Delta+1/2/2, and a further improvement to 8/5 + s when A = 3. Technically, this is achieved by leveraging the fractional local ratio technique, for a first application in a distributed setting. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Approximation algorithms distributed algorithms Fractional local ratio Maximum weight independent set

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Wiener filters on graphs and distributed implementations

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DIGITAL SIGNAL PROCESSING 2025年 162卷

作者： Zheng, Cong Cheng, Cheng Sun, Qiyu Univ Cent Florida Dept Math Orlando FL 32816 USA Sun Yat Sen Univ Sch Math Guangzhou 510275 Guangdong Peoples R China

In this paper, we introduce Wiener filters to recover deterministic and (wide-band) stationary graph signals from their observations corrupted by random noises, and we propose distributed algorithms to implement the Wiener filters. Furthermore, the proposed algorithms can be implemented on distributed networks in which agents are equipped with a data processing subsystem for limited data storage and computation power, and with a one-hop communication subsystem for direct data exchange only with their adjacent agents. Our simulations indicate that the proposed Wiener filtering procedure works well on estimating synthetic (wide-band) stationary signals and real temperature datasets from their noisy observations.

关键词： Graph Wiener filter Inverse filter Stationary graph signal distributed algorithms Graph signal processing

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distributed Stochastic Projection-Free Algorithm for Constrained Optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2479-2494页

作者： Jiang, Xia Zeng, Xianlin Xie, Lihua Sun, Jian Chen, Jie Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong 999077 Peoples R China Beijing Inst Technol Sch Automat Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China

This article proposes a distributed stochastic projection-free algorithm for large-scale constrained finite-sum optimization whose constraint set is complicated such that the projection onto the constraint set can be expensive. The global cost function is allocated to multiple agents, each of which computes its local stochastic gradients and communicates with its neighbors to solve the global problem. Stochastic gradient methods enable low computational complexity, while they are hard and slow to converge due to the variance caused by random sampling. To construct a convergent distributed stochastic projection-free algorithm, this article incorporates variance reduction and gradient tracking techniques in the Frank-Wolfe (FW) update. We develop a novel sampling rule for the variance reduction technique to reduce the variance introduced by stochastic gradients. Complete and rigorous proofs show that the proposed distributed projection-free algorithm converges with a sublinear convergence rate and enjoys superior complexity guarantees for both convex and nonconvex objective functions. By comparative simulations, we demonstrate the convergence and computational efficiency of the proposed algorithm.

关键词： Optimization Complexity theory Convergence Stochastic processes distributed algorithms Vectors Approximation algorithms Minimization Linear programming distributed databases distributed solver finite-sum optimization Frank-Wolfe (FW) algorithm stochastic gradient variance reduction

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distributed Economic Dispatch With Dynamic Power Demand: An Implicit Dual Gradient Tracking Algorithm Under Random-Triggered Transmission Protocol

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IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第2期40卷 1931-1942页

作者： Ma, Dazhong Xing, Mingqi Li, Yuanzheng Sun, Qiuye Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligence Control Minist Educ China Wuhan 430074 Peoples R China

This paper investigates the distributed economic dispatch problem (EDP) under dynamic power demand in power systems. The dynamic power demand implies that the optimal solution to the EDP changes continuously over time, requiring the algorithm to find and track the optimal solution trajectory rapidly. To address this challenge, an implicit dual gradient tracking algorithm (IDGT) is developed based on the distributed gradient tracking algorithm. The IDGT utilizes state and direction information at historical time intervals to track the optimal solution without the requirement for generator units (GUs) to share the estimation of the average gradient. Furthermore, the paper also analyzes the limitations of the conventional event-triggered scheme under dynamic power demand and proposes a novel random-triggered transmission protocol (RTTP). The communication state of each GU is modeled as a Markov chain, including successful communication, packet loss (unknown but bounded), and no communication. This modeling allows the communication frequency between GUs and neighbors to be adjusted and eliminates the requirement to calculate the complex triggering function. Finally, the effectiveness of the proposed IDGT and RTTP is verified through case studies.

关键词： Power demand Heuristic algorithms Power system dynamics Protocols Vectors distributed algorithms Costs Economic dispatch distributed optimization dynamic power demand event-triggered communication

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distributed Solvers for Network Linear Equations With Scalarized Compression

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2644-2651页

作者： Wang, Lei Ren, Zihao Yuan, Deming Shi, Guodong Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Univ Sydney Australia Ctr Robot Sydney NSW 2006 Australia

distributed computing is fundamental to multiagent systems, with solving distributed linear equations as a typical example. In this article, we study distributed solvers for network linear equations over a network with node-to-node communication messages compressed as scalar values. Our key idea lies in a dimension compression scheme that includes a dimension-compressing vector and a data unfolding step. The compression vector applies to individual node states as an inner product to generate a real-valued message for node communication. In the unfolding step, such scalar message is then plotted along the subspace generated by the compression vector for the local computations. We first present a compressed consensus flow that relies only on such scalarized communication, and show that linear convergence can be achieved with well excited signals for the compression vector. We then employ such a compressed consensus flow as a fundamental consensus subroutine to develop distributed continuous-time and discrete-time solvers for network linear equations, and prove their linear convergence properties under scalar node communications. With scalar communications, a direct benefit would be the reduced node-to-node communication channel burden for distributed computing. Numerical examples are presented to illustrate the effectiveness of the established theoretical results.

关键词： Vectors Convergence Signal processing algorithms Heuristic algorithms Electronic mail Consensus algorithm Communication channels distributed algorithms Computational efficiency Australia Compression consensus scalar communication network linear equation

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distributed Dynamic Event-Triggered Optimal Economic Dispatch for Multienergy Systems With Prescribed-Time Convergence

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第6期21卷 4533-4542页

作者： Liu, Lining Yang, Guang-Hong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Northeastern Univ Coll Informat Sci & Engn State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

This article studies the economic dispatch problem for multienergy systems. To minimize the total generation cost subject to the energy demand and various operation constraints, a differential projection-based prescribed-time algorithm is designed to solve the problem. Therein, a dynamic event-triggered communication mechanism is embedded in the execution of the designed algorithm, which reduces the energy consumption of communication. Different from the existing algorithms, the designed algorithm simultaneously possesses the features of asynchronous communication, prescribed-time convergence, free of initialization, etc. Moreover, theoretical analysis results are provided to show that the designed algorithm can converge to the optimal result in a prescribed-time. Finally, the effectiveness and flexibility of the designed algorithm are validated on a 39-32 integrated power-heat system.

关键词： Heuristic algorithms Cogeneration Heating systems Event detection Convergence Linear programming Energy consumption distributed algorithms Costs Smart grids distributed coordination dynamic event-triggered economic dispatch multienergy systems prescribed-time convergence

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Convergence Analysis of distributed Generalized Nash Equilibria Seeking Algorithm With Asynchrony and Delays

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第1期70卷 642-648页

作者： Li, Huaqing Ran, Liang Zheng, Lifeng Li, Zhe Hu, Jinhui Li, Jun Huang, Tingwen Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligent Chongqing 400715 Peoples R China Cent South Univ Dept Automat Changsha 410083 Peoples R China City Univ Hong Kong Dept Biomed Engn Kowloon Hong Kong Peoples R China Shenzhen Univ Adv Technol Fac Comp Sci & Control Engn Shenzhen 518055 Peoples R China

This article considers a class of noncooperative games in which the feasible decision sets of all players are coupled together by a coupled inequality constraint. Adopting the variational inequality formulation of the game, we first introduce a new local edge-based equilibrium condition with full information. Considering challenges when communication delays occur, we then devise an asynchronous distributed algorithm to seek a generalized Nash equilibrium. This asynchronous scheme arbitrarily activates one player to start new computations independently at different iteration instants, which means that the picked player can use the involved outdated information from itself and its neighbors to perform new updates. In theoretical aspect, we provide explicit conditions on algorithm parameters, for instance, the step-sizes to establish a sublinear convergence rate for the synchronous version. Next, the asynchronous algorithm guarantees almost sure convergence in expectation under the same step-size conditions and some standard assumptions. Finally, the viability and performance of the proposed algorithm are demonstrated by numerical studies on the Cournot competition.

关键词： Games Convergence Vectors Heuristic algorithms Delays Standards distributed algorithms Asynchronous distributed algorithm delay communication generalized Nash equilibria (GNE) noncooperative games operator splitting

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DEED-ADMM: A Scalable distributed Algorithm for Economic Dispatch in Multi-Energy Systems With Energy Storage

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11431-11443页

作者： Zhu, Shanying Ding, Tao Chen, Cailian Chow, Mo-Yuen Guan, Xinping Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China Shanghai Jiao Tong Univ UM SJTU Joint Inst Shanghai 200240 Peoples R China

Multi-energy systems with energy storage can coordinate various energy carriers to facilitate the integration of large amounts of distributed energy sources and promote the overall efficiency of energy use, which needs distributed dispatch with the requirement of security and privacy. This paper studies the distributed economic dispatch based on information from neighboring agents only. In order to handle the non-convexity due to the complementarity constraint of energy storage, it is proved that simultaneous charging and discharging is suboptimal for the multi-energy systems. Based on this, an equivalent convex problem is reformulated. A scalable distributed algorithm based on parallel ADMM and dynamic consensus mechanism, termed DEED-ADMM, is then proposed. It is shown that DEED-ADMM is scalable in terms of per-agent energy consumption and computational complexity with centralized methods. Moreover, under general convex cost functions, convergence properties of DEED-ADMM are theoretically analyzed by adopting the Lyapunov-based approach. It is proved that the primal problem and the dual problem can be simultaneously solved. Finally, case studies demonstrate the effectiveness of the proposed algorithm.

关键词： Energy storage Electricity distributed algorithms Cost function Convergence Mercury (metals) Couplings Cogeneration Vectors Matrix decomposition Multi-energy system economic dispatch distributed algorithm Lypapunov-based approach ADMM

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Data-Driven distributed Spectrum Estimation for Linear Time-Invariant Systems

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 1125-1136页

作者： Liu, Shenyu Cortes, Jorge Martinez, Sonia Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Univ Calif San Diego Dept Aerosp & Mech Engn San Diego CA 92093 USA

This article tackles spectrum estimation of a linear time-invariant system by a multiagent network using data. We consider a group of agents that communicate over a strongly connected, aperiodic graph and do not have any knowledge of the system dynamics. Each agent only measures some signals that are linear functions of the system states or inputs, and does not know the functional form of this dependence. The proposed distributed algorithm consists of two steps that rely on the collected data: first, the identification of an unforced trajectory of the system, and second, the estimation of the coefficients of the characteristic polynomial of the system matrix using this unforced trajectory. We show that each step can be formulated as a problem of finding a common solution to a set of linear algebraic equations, which are amenable to distributed algorithmic solutions. We prove that under mild assumptions on the collected data, when the initial condition of the system is random, the proposed distributed algorithm accurately estimates the spectrum with probability 1.

关键词： Spectral analysis distributed algorithms Linear systems Estimation System identification Signal processing algorithms Trajectory Data-driven modeling discrete-time systems distributed processing system identification time invariant systems

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distributed Adaptive Time-Varying Optimization With Global Asymptotic Convergence

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2667-2674页

作者： Jiang, Liangze Wu, Zheng-Guang Wang, Lei Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China

In this note, we study distributed time-varying optimization for a multiagent system. We first focus on a class of time-varying quadratic cost functions, and develop a new distributed algorithm that integrates an average estimator and an adaptive optimizer, with both bridged by a Dead Zone Algorithm. Based on a composite Lyapunov function and finite escape-time analysis, we prove the closed-loop global asymptotic convergence to the optimal solution under mild assumptions. Particularly, the introduction of the estimator relaxes the requirement for the Hessians of cost functions, and the integrated design eliminates the waiting time required in the relevant literature for estimating the global parameter during algorithm implementation. We then extend this result to a more general class of time-varying cost functions. Two examples are used to verify the proposed designs.

关键词： Cost function Convergence Power system dynamics Heuristic algorithms Upper bound Costs Eigenvalues and eigenfunctions distributed algorithms Vectors Time-varying systems Adaptive gain distributed optimization global asymptotic convergence time-varying cost functions

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