检索结果-内蒙古大学图书馆

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

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A Dual Inexact Nonsmooth Newton Method for distributed optimization

引用

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

来源：评论

学校读者我要写书评

暂无评论

Privacy-preserving distributed optimization for economic dispatch in smart grids

引用

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

来源：评论

学校读者我要写书评

暂无评论

A stochastic gradient tracking algorithm with adaptive momentum for distributed optimization

引用

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

来源：评论

学校读者我要写书评

暂无评论

Primal-Dual Fixed Point Algorithms Based on Adapted Metric for distributed optimization With Bounded Communication Delays

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第4期70卷 2212-2227页

作者： Su, Yanxu Wang, Qingling Sun, Changyin Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China Minist Educ Engn Res Ctr Autonomous Unmanned Syst Technol Hefei 230601 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China

This article concentrates on distributed optimization over networks with communication delays. Each subsystem in the network performs its local updates by using the information received from its neighbors, be it possibly outdated. The communication delays with respect to different neighbors are assumed to be arbitrary but bounded. The objective function consists of a twice differentiable coupling term and an aggregated private term. The private function of each subsystem is the sum of two possibly nonsmooth terms, one of which is composed of a linear mapping. We propose a primal-dual fixed point algorithm framework based on the adapted metric for two scenarios where the coupling among subsystems is only enacted by the global objective function and enforced both by the global objective function and the linear mapping. The adapted metric method utilizes an adequate quadratic approximation of the global objective function as the updating step-size to exploit the second-order information. Under some mild assumptions, the convergence of the proposed algorithms is rigorously analyzed based on the quasi-Fej & eacute;r monotonicity. The numerical simulation verifies the correctness and effectiveness of the proposed algorithms.

关键词： Delays optimization Couplings Measurement Convergence Linear programming Vectors Synchronization Symmetric matrices Signal processing algorithms Adapted metric methods asynchronous algorithms distributed optimization primal-dual fixed point (PDFP) algorithms

来源：评论

学校读者我要写书评

暂无评论

Two-Group distributed optimization Under Cooperative-Collaborative Networks With Linear Convergence

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Dong, Ziwei Mao, Shuai Du, Wei Jin, Yaochu Tang, Yang East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Nantong Univ Dept Elect Engn Nantong 226019 Peoples R China Westlake Univ Sch Engn Dept Artificial Intelligence Trustworthy & Gen AI Lab Hangzhou 310030 Peoples R China

This manuscript considers distributed optimization problems in systems with cooperative-collaborative relationships, involving two groups of nodes, each with its own optimization problem, but with a coupled communication topology. For the signed graph representing the cooperation and collaboration between agents, this manuscript introduces DIG-JOR, a discrete-time distributed algorithm that consists of three key modules: an inexact consensus and gradient descent module, a group gradient-tracking module, and a dynamic Jacobi over-relaxation (JOR) inverse-tracking module. To support the convergence analysis of the distributed optimization algorithm, this manuscript proposes the Multi-Loop Small Gain Theorem. Under the assumption of strong convexity and with appropriately chosen step sizes, it is proved that the DIG-JOR algorithm converges to the optimal solutions of both groups at an R-linear rate. The theoretical results are validated through a simulation example.

关键词： distributed optimization cooperative-collaborative network multi-loop small gain theorem linear convergence rate

来源：评论

学校读者我要写书评

暂无评论

Consensus-Based distributed optimization for Multiagent Systems Over Multiplex Networks

引用

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 1040-1051页

作者： Rodriguez-Camargo, Christian David Urquijo-Rodriguez, Andres F. Mojica-Nava, Eduardo UCL Dept Phys & Astron Atom Mol Opt & Positron Phys AMOPP Grp London WC1E 6BT England Univ Nacl Colombia Bogota Colombia Corp Univ Minuto Dios Ctr Estudios Ind & Logist Prod Para Productividad Ind Engn Program Bogota 111321 Colombia Univ Nacl Colombia Dept Phys Grp Superconduct & Nanotecnol & Grp Opt Informac C Bogota 111321 Colombia Univ Nacl Colombia Dept Elect & Elect Engn Bogota 111321 Colombia

Multilayer networks provide a more comprehensive framework for exploring real-world and engineering systems than traditional single-layer networks consisting of multiple interacting networks. However, despite significant research on distributed optimization for single-layer networks, similar progress is lacking for multilayer systems. This article proposes two algorithms for distributed optimization problems in multiplex networks using the supra-Laplacian matrix and its diffusion dynamics. The algorithms include a distributed saddle-point algorithm and its variation as a distributed gradient descent algorithm. By relating consensus and diffusion dynamics, we obtain the multiplex supra-Laplacian matrix. We extend the distributed gradient descent algorithm for multiplex networks using this matrix and analyze the convergence of both algorithms with several theoretical results. Numerical examples validate our proposed algorithms, and we explore the impact of interlayer diffusion on consensus time. We also present a coordinated dispatch for interdependent infrastructure networks (energy-gas) to demonstrate the application of the proposed framework to real engineering problems.

关键词： Multiplexing optimization Heuristic algorithms Nonhomogeneous media Network systems Multi-agent systems Control systems Physics Laplace equations Convex functions Diffusion distributed optimization multiplex networks saddle-point flow

来源：评论

学校读者我要写书评

暂无评论

Accelerated Nesterov Design for distributed optimization Over Directed Graphs

引用

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2025年第4期23卷 1058-1068页

作者： Zhang, Zhoubin Yin, Xiaoqi Fan, Yuan Cheng, Songsong Anhui Univ Sch Elect Engn & Automat Hefei 230601 Anhui Peoples R China

distributed gradient methods are pivotal in optimizing large-scale systems, with diverse applications spanning formation control, resource allocation, and robust game. This paper focuses on designing an accelerated discrete-time algorithm to solve distributed optimization problems over directed networks. Each agent in the network is equipped with its individual strongly convex cost function. The primary objective is to minimize the global cost function, computed as the average of all local cost functions while adhering to the constraints imposed by the network connection structure. We present a novel discrete-time Accelerated distributed Nesterov gradient descent algorithm for directed networks, called ADND. This algorithm incorporates gradient tracking to enhance convergence rate and accuracy. Utilizing row and column stochastic matrices simultaneously effectively broadens its scope of applicability and offers an explicit convergence rate. The findings of this paper are validated through some numerical simulation.

关键词： Directed graph distributed optimization exponential convergence gradient tracking Nesterov

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：