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22nd World Congress of the International Federation of Automatic Control (IFAC)

作者： Guay, Martin Queens Univ Dept Chem Engn Kingston ON Canada

ISBN: (纸本)9781713872344

In this study, a Newton consensus method is proposed for the distributed optimization of a multi-agent systems operating over strongly connected digraphs. The approach proposes an approximate Newton step for both the primal and dual problems that can be implemented in a completely decentralized fashion. The asymmetry in the communication network is addressed by computing an approximate Newton step that only requires the out-Laplacian. The proposed Newton consensus approach does not require the exchange of derivative information between agents. In addition, the optimization approach avoids the explicit inversion of the approximate Hessian information for the computation of the Newton step. A simulation study demonstrates the effectiveness of the technique.

关键词： Newton consensus distributed optimization multi-agent systems

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distributed optimization Algorithm for Multi-agent System with Time-Varying Communication Delay Based on the Game Theory 19th

Distributed Optimization Algorithm for Multi-agent System wi...

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19th Chinese Intelligent Systems Conference

作者： Wang, Chen Zhu, Rui Wang, Fuyong Liu, Zhongxin Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ China Chongqing 400044 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China

ISBN: (纸本)9789819968497;9789819968473;9789819968466

In this paper, the distributed optimization problem for multi-agent system with time-varying communication delay tau(t) is studied based on game theory. Firstly, the distributed optimization problem min(x) phi(x) is modeled as a state based ordinal potential game model G. Then, the existence and validity of the Nash equilibrium in the game model are verified. In addition, a revenue-based strategy learning algorithm is designed under topology network with tau(t) to find the Nash equilibrium. Finally, a numerical simulation illustrates the results.

关键词： Multi-agent systems distributed optimization Game theory Time-varying communication delay Strategy learning algorithm

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Linear convergence of event-triggered distributed optimization with metric subregularity condition

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ASIAN JOURNAL OF CONTROL 2025年第2期27卷 750-764页

作者： Yu, Xin Cheng, Songsong Qiu, Jianbin Fan, Yuan AnHui Univ Sch Elect Engn & Automat Hefei Peoples R China Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin Peoples R China

This paper designs a continuous-time algorithm with event-triggered communication (ETC) for solving a class of distributed convex optimization problems with a metric subregularity condition. First, we develop an event-triggered continuous-time optimization algorithm to overcome the bandwidth limitation of multi-agent systems. Besides, with the aid of Lyapunov theory, we prove that the distributed event-triggered algorithm converges to the optimum set with an exact linear convergence rate, without the strongly convex condition. Moreover, we provide the discrete version of the continuous-time algorithm and show its exact linear convergence rate. Finally, we give a comparison example to validate the effectiveness of the designed algorithm in communication resource saving.

关键词： distributed optimization event-triggered linear convergence metric subregularity primal-dual

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Prescribed-Time Event-Triggered distributed optimization With Privacy Protection Over Directed Networks

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2025年第0期

作者： Shi, Xinli Fan, Deru Wang, Kang Wan, Ying Wen, Guanghui Southeast Univ Sch Cyber Sci Engn Nanjing Peoples R China Southeast Univ Sch Math Nanjing Peoples R China

This paper focuses on privacy-preserving distributed convex optimization across directed graphs within a prescribed-time. To reduce the communication cost and achieve fast convergence, we propose a novel event-triggered and prescribed-time convergent distributed optimization algorithm built upon an extended Zero-Gradient-Sum method with free initialization. Specifically, we formulate event-triggering conditions for each agent, ensuring that inter-agent communication occurs solely upon meeting these conditions, thus significantly reducing communication costs. By the Lyapunov stability theory, the proposed algorithm is proven to achieve an accurate convergence to the optima within a prescribed-time. Moreover, we establish the absence of Zeno behavior throughout any arbitrary period except the specified convergence time. When the environment exists, eavesdropping attacks, we further provide a privacy-preserving prescribed-time event-triggered distributed algorithm based on state and objective decomposition. Finally, two comprehensive simulations demonstrate the performance of our proposed algorithm.

关键词： distributed optimization event-triggered scheme prescribed-time convergence privacy protection zero-gradient-sum method

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Prescribed-time distributed optimization for multi-agent system subject to inequality constraints

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ASIAN JOURNAL OF CONTROL 2025年第3期27卷 1530-1540页

作者： Zhao, Xiao-Wen Zhou, Chen-An Liu, Zhi-Wei Lai, Qiang Ge, Ming-Feng Hefei Univ Technol Sch Math Hefei 230009 Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China East China Jiaotong Univ Sch Elect & Automat Engn Nanchang Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat Wuhan Peoples R China

This paper investigates the prescribed-time distributed optimization control problem of multi-agent system subject to inequality constraints over directed networks. The log-barrier penalty function method is utilized to cope with the effects of inequality constraints. First, the relationship between the original problem and the processed unconstrained optimization problem is presented. By means of the zero-gradient-sum method and the integral sliding mode technique, the controller is designed such that the state of each agent converges to a vicinity of the optimal solution in a specified time. The interior point theorem ensures that the inequality constraints always hold. Numerical example is provided to verify the effectiveness of the theoretical analysis.

关键词： constraints distributed optimization multi-agent systems prescribed-time

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Passivity of nabla fractional order systems and its application on distributed optimization

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2025年 146卷

作者： Xu, Haoran Chen, Rui Ni, Xintong Wei, Yiheng Southeast Univ Sch Math Nanjing 211189 Peoples R China

While the passivity of integer-order systems has been extensively analyzed, recent focus has shifted toward exploring the passivity of fractional-order systems. However, a clear definition of Nabla Fractional Order Systems (NFOSs) has not yet been established. In this work, the concepts of passivity, dissipativity, and finite-gain L2,alpha stability are extended to NFOSs, and relevant theories are proposed. Utilizing nabla fractional calculus and these proposed theories, a passivity-based approach is developed to study distributed optimization in nonlinear multi-agent systems experiencing observational disturbances.

关键词： Passivity Nabla fractional order systems L2,alpha stability Lyapunov stable analysis distributed optimization

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A Dual Inexact Nonsmooth Newton Method for distributed optimization

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 188-203页

作者： Niu, Dunbiao Hong, Yiguang Song, Enbin Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Natl Key Lab Autonomous Intelligent Unmanned Syst Shanghai 201804 Peoples R China Sichuan Univ Coll Math Chengdu 610065 Sichuan Peoples R China

In this paper, we propose a novel dual inexact nonsmooth Newton (DINN) method for solving a distributed optimization problem, which aims to minimize a sum of cost functions located among agents by communicating only with their neighboring agents over a network. Our method is based on the Lagrange dual of an appropriately formulated primal problem created by introducing local variables for each agent and enforcing a consensus constraint among these variables. Due to the decomposed structure of the dual problem, the DINN method guarantees a superlinear (or even quadratic) convergence rate for both the primal and dual iteration sequences, achieving the same convergence rate as its centralized counterpart. Furthermore, by exploiting the special structure of the dual generalized Hessian, we design a distributed iterative method based on Nesterov's acceleration technique to approximate the dual Newton direction with suitable precision. Moreover, in contrast to existing second-order methods, the DINN method relaxes the requirement for the objective function to be twice continuously differentiable by using the linear Newton approximation of its gradient. This expands the potential applications of distributed Newton methods. Numerical experiments demonstrate that the DINN method outperforms the current state-of-the-art distributed optimization methods.

关键词： Convergence Jacobian matrices Vectors Newton method Linear programming Costs Signal processing algorithms Approximation algorithms distributed databases Cost function Inexact nonsmooth Newton method distributed optimization superlinear convergence rate communication cost

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Privacy-preserving distributed optimization for economic dispatch in smart grids

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AUTOMATICA 2025年 177卷

作者： An, Wenjing Ding, Derui Wang, Zidong Liu, Qinyuan Dong, Hongli Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai 200093 Peoples R China Swinburne Univ Technol Sch Engn Melbourne Vic 3122 Australia Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middx England Tongji Univ Sch Comp Sci & Technol Shanghai 201804 Peoples R China Tongji Univ Minist Educ Key Lab Embedded Syst & Serv Comp Shanghai 200092 Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing 163318 Peoples R China

This paper discusses a distributed economic dispatch problem (EDP) of smart grids while preventing sensitive information from being leaked during the communication process. In response to the problem, a novel privacy-preserving distributed economic dispatch strategy is developed via adding an exponentially decaying random noise to minimize the total cost of the grid while ensuring the privacy of sensitive state information. The quantitative relationship between the privacy and the estimation accuracy of eavesdroppers is profoundly disclosed in the framework of (sigma, v)-data-privacy. Furthermore, a sufficient condition on the iteration step size is achieved to ensure that the welldesigned algorithm can converge to the optimal value of the addressed EDP exactly by resorting to the classical Lyapunov stability theory. Finally, simulation results verify the effectiveness of the carefully constructed privacy-preserving scheme. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： distributed optimization Economic dispatch problems Data privacy Smart grids

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A stochastic gradient tracking algorithm with adaptive momentum for distributed optimization

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NEUROCOMPUTING 2025年 637卷

作者： Li, Yantao Hu, Hanqing Zhang, Keke Lu, Qingguo Deng, Shaojiang Li, Huaqing Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

In this paper, we study distributed optimization problems where each node owns a local convex cost function calculated as the average of multiple constituent functions, and multiple nodes collaborate to minimize the finite sum of these local functions. Reviewing existing work, distributed optimization methods with adaptive momentum that consider reducing computation costs have not yet been explored. To this aim, we propose a gradient tracking stochastic distributed optimization algorithm with adaptive momentum, called GTSADAM. GTSADAM combines the distributed adaptive momentum method for faster convergence with the variance reduction mechanism to reduce computation costs. We provide a convergence analysis indicating that, under certain step size conditions, GTSADAM achieves linear convergence in the mean to the exact optimal solution when each constituent function is strongly convex and smooth. Moreover, GTSADAM maintains the acceleration efficiency of adaptive momentum while minimizing computation costs, which is confirmed by numerical simulations, and its performance is better than that of existing methods.

关键词： distributed optimization Gradient tracking Variance reduction Adaptive momentum estimation Linear convergence

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distributed optimization of Smart Grid Assets for Grid Operation Preserving Power System Operator Sovereignty

Distributed Optimization of Smart Grid Assets for Grid Opera...

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IEEE Belgrade PowerTech Conference

作者： Meinecke, Steffen Stock, David Sebastian Braun, Martin Univ Kassel Dept Energy Management & Power Syst Operat E2n Kassel Germany Fraunhofer Inst Energy Econ & Energy Syst Technol Kassel Germany

ISBN: (纸本)9781665487788

distributed optimization methods promise to coordinate power system operators to improve the overall grid operation. This paper analyzes and extends existing distributed optimization methods for reactive power dispatch of multiple transmission system operators (TSOs) and distribution system operators (DSOs). For smart grid operations, reactive power provision of distributed energy resources (DERs), voltage magnitude setpoints of power plants and transformer tap positions are optimized by mixed-integer nonlinear programming (MINLP). The methods compared let the system operators (SOs) optimize and operate autonomously to preserve their sovereignty. Simulation results for a German benchmark grid with 261 buses show that all six methods compared have the potential to improve non-coordinated grid operation. One method achieves even near-optimal results. However, a discussion suggests only the investigated sequential method as a promising method for implementation in real grids, especially due to the uncertain convergence behavior of the other methods. Improving the performance of the sequential method and adding a balancing between transmission and distribution system operators' interests are pointed out for future research.

关键词： Optimal Power Flow distributed optimization Transmission System Operator Distribution System Operator Multi-Voltage Level

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