检索结果-内蒙古大学图书馆

distributed convex optimization based on zero-gradient-sum algorithm under switching topology

IET CONTROL THEORY AND APPLICATIONS 2023年第12期17卷 1611-1624页

作者： Tan, Manchun Ren, Junwu Ye, Lei Xiang, Jianglian Jinan Univ Coll Informat Sci & Technol Guangzhou Peoples R China Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China

This paper designs a finite-time convergence protocol and an event-triggered control protocol based on Zero-Gradient-Sum (ZGS) algorithm under stochastic switching undirected topology, respectively, which greatly expands the theory of continuous-time distributed optimization algorithms. With finite-time stability and Lyapunov stability analysis, it is illustrated that the proposed method can finite-time converge to the optimal solution of distributed unconstrained convex optimization problem and overcome the disturbances of the switching communication networks. In addition, the event-triggered mechanism can effectively reduce the network burden and communication cost as well as avoid Zeno behaviour. Finally, two numerical simulations verify the advantages and effectiveness of these methods.

关键词： asymptotic stability convergence continuous time systems distributed algorithms

来源：评论

学校读者我要写书评

暂无评论

distributed Online Aggregative Optimization for Dynamic Multirobot Coordination

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第6期68卷 3736-3743页

作者： Carnevale, Guido Camisa, Andrea Notarstefano, Giuseppe Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy

This article focuses on an online version of the emerging distributed constrained aggregative optimization framework, which is particularly suited for applications arising in cooperative robotics. Agents in a network want to minimize the sum of local cost functions, each one depending both on a local optimization variable, subject to a local constraint, and on an aggregated version of all the variables (e.g., the mean). We focus on a challenging online scenario in which the cost, the aggregation functions, and the constraints can all change over time, thus enlarging the class of captured applications. Inspired by an existing scheme, we propose a distributed algorithm with constant step size, named projected aggregative tracking, to solve the online optimization problem. We prove that the dynamic regret is bounded by a constant term and a term related to time variations. Moreover, in the static case (i.e., with constant cost and constraints), the solution estimates are proved to converge with a linear rate to the optimal solution. Finally, numerical examples show the efficacy of the proposed approach on a robotic surveillance scenario.

关键词： Cost function Costs Heuristic algorithms distributed algorithms Surveillance Linear programming Mirrors Cooperative control distributed optimization optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

S-NEAR-DGD: A Flexible distributed Stochastic Gradient Method for Inexact Communication

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第2期68卷 1281-1287页

作者： Iakovidou, Charikleia Wei, Ermin Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA

We present and analyze a stochastic distributed method (S-NEAR-DGD) that can tolerate inexact computation and inaccurate information exchange to alleviate the problems of costly gradient evaluations and bandwidth-limited communication in large-scale systems. Our method is based on a class of flexible, distributed first-order algorithms that allow for the tradeoff of computation and communication to best accommodate the application setting. We assume that the information exchanged between nodes is subject to random distortion and that only stochastic approximations of the true gradients are available. Our theoretical results prove that the proposed algorithm converges linearly in expectation to a neighborhood of the optimal solution for strongly convex objective functions with Lipschitz gradients. We characterize the dependence of this neighborhood on algorithm and network parameters, the quality of the communication channel and the precision of the stochastic gradient approximations used. Finally, we provide numerical results to evaluate the empirical performance of our method.

关键词： Optimization Convergence distributed algorithms Radio frequency Approximation algorithms Quantization (signal) Probabilistic logic distributed optimization network optimization quantization stochastic optimization

来源：评论

学校读者我要写书评

暂无评论

distributed Banach-Picard Iteration for Locally Contractive Maps

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第2期68卷 1275-1280页

作者： Andrade, Francisco Figueiredo, Mario A. T. Xavier, Joao Univ Lisbon Inst Super Tecn P-1049001 Lisbon Portugal Inst Telecomunicacoes P-1049001 Lisbon Portugal Inst Syst & Robot Lab Robot & Engn Syst P-1049001 Lisbon Portugal

The Banach-Picard iteration is widely used to find fixed points of locally contractive (LC) maps. This article extends the Banach-Picard iteration to distributed settings;specifically, we assume the map of which the fixed point is sought to be the average of individual (not necessarily LC) maps held by a set of agents linked by a communication network. An additional difficulty is that the LC map is not assumed to come from an underlying optimization problem, which prevents exploiting strong global properties, such as convexity or Lipschitzianity. Yet, we propose a distributed algorithm and prove its convergence, in fact showing that it maintains the linear rate of the standard Banach-Picard iteration for the average LC map. As another contribution, our proof imports tools from perturbation theory of linear operators, which, to the best of our knowledge, are scarcely exploited in the theory of distributed computation.

关键词： Convergence distributed algorithms Optimization Jacobian matrices Perturbation methods distributed databases Symmetric matrices Banach-Picard iteration consensus distributed computation fixed points perturbation theory (PT) of linear operators

来源：评论

学校读者我要写书评

暂无评论

On Liveness Enforcement of distributed Petri Net Systems

引用

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第6期68卷 3776-3782页

作者： Clavel, Daniel Mahulea, Cristian Silva, Manuel Univ Zaragoza Aragon Inst Engn Res I3A Dept Comp Sci & Syst Engn Zaragoza 50009 Spain

In this article, we consider the liveness enforcement problem in a class of Petri nets (PNs) modeling distributed systems. They are called synchronized sequential processes. The presented design algorithm is based on the construction of a control PN, an abstraction of the relations of the T-semiflows, and buffers of the original nonstructurally live PN. The control PN evolves in parallel with the system, avoiding the firing of transitions that may lead the system to nonliveness. Four algorithms are presented, one allowing for the computation of the control PN and three ensuring its liveness.

关键词： Petri nets Synchronization Force Process control Medical tests Control systems Time complexity Discrete-event systems distributed algorithms system testing

来源：评论

学校读者我要写书评

暂无评论

Maximal Dissent: A State-Dependent Way to Agree in distributed Convex Optimization

引用

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2023年第4期10卷 1783-1795页

作者： Verma, Ashwin Vasconcelos, Marcos M. Mitra, Urbashi Touri, Behrouz Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92093 USA Florida State Univ FSU Coll Engn Dept Elect & Comp Engn FAMU Tallahassee FL 32306 USA Univ Southern Calif Ming Hsieh Dept Elect Engn Los Angeles CA 90007 USA

Consider a set of agents collaboratively solving a distributed convex optimization problem asynchronously under stringent communication constraints. When an agent becomes active, it is allowed to communicate with only one of its neighbors. In this article, we propose new state-dependent gossip algorithms where the agents with maximal dissent average their estimates. We prove the almost sure convergence of max-dissent subgradient methods using a unified framework applicable to other state-dependent distributed optimization algorithms. Furthermore, our proof technique bypasses the need to establish the information flow between any two agents within a time interval of uniform length by intelligently studying the convergence properties of the Lyapunov function used in our analysis.

关键词： Optimization Convergence Convex functions Standards Network systems Lyapunov methods Control systems Consensus algorithm distributed algorithms optimization

来源：评论

学校读者我要写书评

暂无评论

distributed Nash Equilibrium Seeking for Aggregative Games With Quantization Constraints

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2023年第6期70卷 2537-2549页

作者： Pei, Yingqing Tao, Ye Gu, Haibo Lu, Jinhu Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Zhongguancun Lab Beijing 100094 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

The problem of seeking Nash equilibrium (NE) based on aggregative games under quantization constraints is full of challenges. Although the NE seeking algorithm in continuous-time systems has been studied, this problem in discrete-time systems still needs to be solved urgently. To address this problem, three distributed algorithms are first proposed under three quantization cases, adaptive, random, and time-varying quantizations, based on doubly stochastic communication topology networks. Then, the actions of players would eventually converge to NE under the conditions of vanishing step size and strong monotonicity are proved. Moreover, the convergence rate of the three quantization cases are analyzed, respectively. Finally, numerical experiments are implemented on plug-in hybrid electric vehicles (PHEVs) to validate the effectiveness of the proposed distributed algorithms. Comparing the convergence rates of the three proposed algorithms, the convergence effect of the adaptive quantization is better than that of the other two quantization cases.

关键词： Quantization (signal) Games Convergence distributed algorithms Heuristic algorithms Nash equilibrium Uncertainty Aggregative game quantization distributed algorithm multi-agent system

来源：评论

学校读者我要写书评

暂无评论

distributed Banach-Picard Iteration: Application to distributed Parameter Estimation and PCA

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 17-30页

We recently proposed an algorithmic framework, distributed Banach-Picard iteration (DBPI), allowing a set of agents linked by a communication network to find a fixed point of a map that: (a) is the average of individual maps held by said agents;(b) is locally contractive (LC). Given such a map, DBPI yields a distributed algorithm provably inheriting the local linear convergence (LLC) of the standard Banach-Picard iteration for the centralized (average) map. Here, we instantiate DBPI in two classical problems, which amounts to proving that the conditions guaranteeing the LLC of DBPI hold. First, taking Sanger's algorithm for principal component analysis (PCA), we show that it corresponds to iterating an LC map that can be written as the average of local maps held by agents with private data subsets. Applying DBPI then recovers a previous distributed PCA algorithm, which lacked a convergence proof, thus closing that gap. In the second instantiation, we show that a variant of the expectation-maximization (EM) algorithm for parameter estimation from noisy, faulty measurements in sensor networks can be written as iterating an LC map that is the average of local maps. Consequently, the DBPI framework yields a distributed algorithm automatically inheriting the LLC guarantee of its centralized counterpart. Verifying the LC condition for EM is nontrivial (as the underlying operator depends on random samples) and a contribution in itself, possibly of independent interest. Finally, we illustrate experimentally the linear convergence of the proposed distributed EM algorithm, contrasting with the sub-linear rate of the previous version.

关键词： Task analysis Convergence Principal component analysis Signal processing algorithms distributed algorithms Dimensionality reduction Computational modeling distributed computation banach-picard iteration fixed points distributed EM distributed PCA consensus

来源：评论

学校读者我要写书评

暂无评论

GTAdam: Gradient Tracking With Adaptive Momentum for distributed Online Optimization

引用

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2023年第3期10卷 1436-1448页

作者： Carnevale, Guido Farina, Francesco Notarnicola, Ivano Notarstefano, Giuseppe Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy

This article deals with a network of computing agents aiming to solve an online optimization problem in a distributed fashion, i.e., by means of local computation and communication, without any central coordinator. We propose the gradient tracking with an adaptive momentum estimation (GTAdam) distributed algorithm, which combines a gradient tracking mechanism with first- and second-order momentum estimates of the gradient. The algorithm is analyzed in the online setting for strongly convex cost functions with Lipschitz continuous gradients. We provide an upper bound for the dynamic regret given by a term related to the initial conditions and another term related to the temporal variations of the objective functions. Moreover, a linear convergence rate is guaranteed in the static setup. The algorithm is tested on a time-varying classification problem, on a (moving) target localization problem, and in a stochastic optimization setup from image classification. In these numerical experiments from multiagent learning, GTAdam outperforms state-of-the-art distributed optimization methods.

关键词： distributed algorithms distributed optimization multiagent systems networked systems optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

distributed Fixed-Time Optimal Resource Management for Microgrids

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2023年第1期20卷 404-412页

作者： Liu, Li-Ning Yang, Guang-Hong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

This article studies the problem of distributed cooperative resource management for Microgrids. In order to maximize the social welfare, a distributed fixed-time consensus algorithm is first proposed to integrate economic dispatch and demand response, which optimally assigns the energy between generation participants and load participants. Based on the designed algorithm, each participant is able to quickly obtain its optimal operation and only requires partial calculation and communication without a central control coordination, thus it offers high flexibility, strong robustness, and better privacy. Moreover, compared with the existing distributed optimization algorithms, the proposed algorithm simultaneously possesses the advantages of fixed-time consensus, no initialization conditions, and supporting switching topology, etc. Furthermore, it is proved that each participant can achieve consensus in a fixed-time manner and converge to the global optimal point if the parameters of the algorithm satisfy some conditions. Finally, numerical simulations in the 6-Bus and the IEEE 30-Bus systems are provided to validate the effectiveness of the presented algorithm.

关键词： Microgrids Resource management Convergence Communication networks Switches distributed algorithms Costs distributed algorithm fixed-time resource management demand response microgrids cyber-physical system

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：