检索结果-内蒙古大学图书馆

distributed constrained optimization Algorithms for Drones

DRONES 2025年第1期9卷 36-36页

作者： Liu, Hongzhe Southeast Univ Sch Math Nanjing 210096 Peoples R China

The present study addresses a critical issue within the realm of drones: the challenge of distributed constrained optimization. Our research delves into an optimization scenario where the decision variable is confined to a closed convex set. The primary objective is to develop a distributed algorithm capable of tackling this optimization problem. To achieve this, we have crafted distributed algorithms for both balanced graphs and unbalanced graphs, with the method of feasible direction employed to address the considered constraint, and the method of estimating left eigenvector to address the unbalance, incorporating momentum elements. We have demonstrated that the algorithms exhibit linear convergence when the local objective functions are both smooth and strongly convex, and when the step-sizes are appropriately chosen. Additionally, the simulation outcomes validate the efficacy of our distributed algorithms.

关键词： drones distributed constrained optimization momentum terms linear convergence rate

来源：评论

学校读者我要写书评

暂无评论

distributed constrained optimization for Bayesian Opportunistic Visual Sensing

引用

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2014年第6期22卷 2302-2318页

作者： Morye, Akshay A. Ding, Chong Roy-Chowdhury, Amit K. Farrell, Jay A. Univ Calif Riverside Dept Elect Engn Riverside CA 92521 USA Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA

We propose a control mechanism to obtain opportunistic high resolution facial imagery, via distributed constrained optimization of the pan, tilt, zoom (PTZ) parameters for each camera in a sensor network. The objective function of the optimization problem quantifies the per camera per target image quality. The tracking constraints, which are a lower bound on the information about the estimated position for each target, define the feasible PTZ parameter space. Each camera alters its own PTZ settings. All cameras use information broadcast by neighboring cameras such that the PTZ parameters of all cameras are simultaneously optimized relative to the global objective. At certain times of opportunity, due to the configuration of the targets relative to the cameras, and the fact that each camera may track many targets, the camera network may be able to reconfigure itself to achieve the tracking specification for all targets with remaining degrees of freedom that can be used to obtain high-res facial images from desirable aspect angles for certain targets. The challenge is to define algorithms to automatically find these time instants, the appropriate imaging camera, and the appropriate parameter settings for all cameras to capitalize on these opportunities. The solution proposed herein involves a Bayesian formulation in a game theoretic setting. The Bayesian formulation automatically trades off objective maximization versus the risk of losing track of any target. This paper describes the problem and solution formulations, design of aligned local and global objective functions and the inequality constraint set, and development of a distributed Lagrangian consensus algorithm that allows cameras to exchange information and asymptotically converge on a pair of primal-dual optimal solutions. This paper presents the theoretical solution along with the simulation results.

关键词： Camera sensor networks cooperative control distributed constrained optimization

来源：评论

学校读者我要写书评

暂无评论

Resilient Penalty Function Method for distributed constrained optimization under Byzantine Attack

引用

INFORMATION SCIENCES 2022年 596卷 362-379页

作者： Xu, Chentao Liu, Qingshan Huang, Tingwen Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Sch Math Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Texas A&M Univ Qatar Dept Sci Program Doha 23874 Qatar

distributed optimization algorithms have the advantages of privacy protection and parallel computing. However, the distributed nature of these algorithms makes the system vulner-able to external attacks. This paper presents two penalty function based resilient algo-rithms for constrained distributed optimization under static and dynamic attacks. The objective function of the optimization problem is extended to nonsmooth ones and the convergence of the proposed algorithms in this case are proved under some mild condi-tions. Simulation experiments are performed and compared with some existing resilient primal-dual optimization algorithms using median-based mean estimator. For static attack, the proposed algorithm has better performance and faster convergence rate in the simulation experiments. For dynamic attack, the proposed algorithm has better perfor-mance and robustness in the simulation experiments, which illustrate that the proposed algorithms are more effective. (c) 2022 Elsevier Inc. All rights reserved.

关键词： distributed constrained optimization penalty function method multi-agent system Byzantine attack

来源：评论

学校读者我要写书评

暂无评论

Primal-dual algorithm for distributed constrained optimization

引用

SYSTEMS & CONTROL LETTERS 2016年第0期96卷 110-117页

作者： Lei, Jinlong Chen, Han-Fu Fang, Hai-Tao Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100864 Peoples R China

The paper studies a distributed constrained optimization problem, where multiple agents connected in a network collectively minimize the sum of individual objective functions subject to a global constraint being an intersection of the local constraints assigned to the agents. Based on the augmented Lagrange method, a distributed primal-dual algorithm with a projection operation included is proposed to solve the problem. It is shown that with appropriately chosen constant step size,,the local estimates derived at all agents asymptotically reach a consensus at an optimal solution. In addition, the value of the cost function at the time-averaged estimate converges with rate O(1/k) to the optimal value for the unconstrained problem. By these properties, the proposed primal-dual algorithm is distinguished from the existing algorithms for distributed constrained optimization. The theoretical analysis is justified by numerical simulations. (C) 2016 Elsevier B.V. All rights reserved.

关键词： distributed constrained optimization Primal-dual algorithm Augmented Lagrange method Multi-agent network

来源：评论

学校读者我要写书评

暂无评论

A Predefined-Time Consensus Algorithm of Multi-Agent System for distributed constrained optimization

引用

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 957-968页

作者： Li, Kaixuan Hu, Qilong Liu, Qingshan Zeng, Zhigang Cheng, Fan Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Sch Math Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Anhui Univ Sch Artificial Intelligence Inst Informat Mat & Intelligent Sensing Lab Anhui Hefei 230601 Peoples R China

This article proposes a multi-agent system with predefined-time consensus to solve distributed optimization problem subject to inconsistent constraint sets. Under some derived conditions, the predefined-time consensus of multi-agent system is guaranteed. Meanwhile, the states of agents are convergent with consensus to an optimal solution of the distributed constrained optimization. Compared to the system with finite time consensus, the proposed system has a fixed time consensus that can be estimated in advance. The suggested system for distributed constrained optimization not requires objective functions have a specific form and smooth. It suitable for the problem with bounded sub-differential of objective function and unbound gradient of objective function. Simulation results validate the theoretical analysis.

关键词： Linear programming optimization Convergence Resource management Privacy Multi-agent systems Sensors Optimal solution multi-agent system distributed constrained optimization fixed-time consensus communication network

来源：评论

学校读者我要写书评

暂无评论

Initialization-free distributed constrained optimization algorithms with a pre-specified time 62

Initialization-free distributed constrained optimization alg...

引用

62nd IEEE Conference on Decision and Control (CDC)

作者： Shi, Xiasheng Mu, Chaoxu Sun, Changyin Ren, Lu Su, Yanxu Anhui Univ Sch Artificial Intelligence Hefei Peoples R China Anhui Univ Minist Educ Engn Res Ctr Autonomous Unmanned Syst Technol Hefei Peoples R China Anhui Univ Anhui Prov Engn Res Ctr Unmanned Syst & Intellig Hefei Peoples R China Dalian Univ Technol Minist Educ Key Lab Intelligent Control & Optimizat Ind Equip Dalian Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9798350301243

The distributed constrained optimization problem over an undirected communication topology is investigated in this study. It focuses on addressing a global coupled equality constraint that applies to all agents. To tackle this problem, a distributed approach with arbitrary initialization is developed by virtue of the aperiodic sampling control idea and the consensus-based multi-agent system(MAS) technology. This approach is developed to address constrained optimization problems within a pre-specified time. In addition, this predefined time is freely defined by users and irrelevant to the initial states, control coefficients, and network structure of systems. The Lyapunov stability theory completes the convergence proof of the developed method. Then, the developed method is extended to handle distributed nonlinear constrained optimization problems. Finally, The availability of two developed methods is demonstrated through two simulation examples.

关键词： distributed constrained optimization pre-specified time nonlinear non-periodical sampling

来源：评论

学校读者我要写书评

暂无评论

Gossip-based distributed stochastic mirror descent for constrained optimization

引用

NEURAL NETWORKS 2024年 175卷 106291页

作者： Fang, Xianju Zhang, Baoyong Yuan, Deming Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China

This paper considers a distributed constrained optimization problem over a multi -agent network in the nonEuclidean sense. The gossip protocol is adopted to relieve the communication burden, which also adapts to the constantly changing topology of the network. Based on this idea, a gossip -based distributed stochastic mirror descent (GB-DSMD) algorithm is proposed to handle the problem under consideration. The performances of GB-DSMD algorithms with constant and diminishing step sizes are analyzed, respectively. When the step size is constant, the error bound between the optimal function value and the expected function value corresponding to the average iteration output of the algorithm is derived. While for the case of the diminishing step size, it is proved that the output of the algorithm uniformly approaches to the optimal value with probability 1. Finally, as a numerical example, the distributed logistic regression is reported to demonstrate the effectiveness of the GB-DSMD algorithm.

关键词： Stochastic mirror descent distributed constrained optimization distributed convex optimization Gossip algorithm

来源：评论

学校读者我要写书评

暂无评论

Momentum-based distributed resource scheduling optimization subject to sector-bound nonlinearity and latency

引用

SYSTEMS & CONTROL LETTERS 2025年 199卷

作者： Doostmohammadian, Mohammadreza Gabidullina, Zulfiya R. Rabiee, Hamid R. Semnan Univ Fac Mech Engn Mechatron Grp Semnan Iran Ctr Int Sci Studies & Collaborat Tehran Iran Kazan Fed Univ Inst Computat Math & Informat Technol Kazan Russia Sharif Univ Technol Comp Engn Dept Tehran Iran

This paper proposes an accelerated consensus-based distributed iterative algorithm for resource allocation and scheduling. The proposed gradient-tracking algorithm introduces an auxiliary variable to add momentum towards the optimal state. We prove that this solution is all-time feasible, implying that the coupling constraint always holds along the algorithm iterative procedure;therefore, the algorithm can be terminated time. This is in contrast to the ADMM-based solutions that meet constraint feasibility asymptotically. Further, we show that the proposed algorithm can handle possible link nonlinearity due to logarithmically-quantized data transmission (or any sign-preserving odd sector-bound nonlinear mapping). We prove convergence uniformly-connected dynamic networks (i.e., a hybrid setup) that may occur in mobile and time-varying agent networks. Further, the latency issue over the network is addressed by proposing delay-tolerant solutions. To our best knowledge, accelerated momentum-based convergence, nonlinear linking, all-time feasibility, uniform network connectivity, and handling (possible) time delays are not altogether addressed in the literature. These contributions make our solution practical in many real-world applications.

关键词： distributed constrained optimization Consensus Graph theory Convex analysis Resource scheduling and allocation

来源：评论

学校读者我要写书评

暂无评论

Solving Highly Cyclic distributed optimization Problems Without Busting the Bank: A Decimation-based Approach

引用

LOGIC JOURNAL OF THE IGPL 2021年第1期29卷 72-95页

作者： Cerquides, Jesus Antonio Rodriguez-Aguilar, Juan Emonet, Remi Picard, Gauthier Artificial Intelligence Res Inst IIIA CSIC Campus UAB Bellaterra 08193 Spain Univ Lyon UJM St Etienne CNRS Inst OptGrad SchLab Hubert Curien UMR 5516 F-42023 St Etienne France Univ Jean Monnet Mines St Etienne Univ Lyon IOGSCNRSUMR 5516 LHCInst Henri FayolDept ISI F-42023 St Etienne France

In the context of solving large distributed constraint optimization problems, belief-propagation and incomplete inference algorithms are candidates of choice. However, in general, when the problem structure is very cyclic, these solution methods suffer from bad performance, due to non-convergence and many exchanged messages. As to improve performances of the MaxSum inference algorithm when solving cyclic constraint optimization problems, we propose here to take inspiration from the belief-propagation-guided decimation used to solve sparse random graphs (k-satisfiability). We propose the novel DECIMAXSUM method, which is parameterized in terms of policies to decide when to trigger decimation, which variables to decimate and which values to assign to decimated variables. Based on an empirical evaluation on a classical constraint optimization benchmarks (graph coloring, random graph and Ising model), some of these combinations of policies, using periodic decimation, cycle detection-based decimation, parallel and non parallel decimation, random or deterministic variable selection and deterministic or random sampling for value selection, outperform state-of-the-art competitors in many settings.

关键词： distributed constrained optimization DCOP decimation belief propagation

来源：评论

学校读者我要写书评

暂无评论

distributed Discrete-Time Convex optimization With Closed Convex Set Constraints: Linearly Convergent Algorithm Design

引用

IEEE TRANSACTIONS ON CYBERNETICS 2024年第4期54卷 2271-2283页

作者： Luan, Meng Wen, Guanghui Liu, Hongzhe Huang, Tingwen Chen, Guanrong Yu, Wenwu Southeast Univ Sch Math Dept Syst Sci Nanjing 211189 Peoples R China Texas A&M Univ Qatar Dept Sci Doha Qatar City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Sch Math Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211102 Peoples R China

The convergence rate and applicability to directed graphs with interaction topologies are two important features for practical applications of distributed optimization algorithms. In this article, a new kind of fast distributed discrete-time algorithms is developed for solving convex optimization problems with closed convex set constraints over directed interaction networks. Under the gradient tracking framework, two distributed algorithms are, respectively, designed over balanced and unbalanced graphs, where momentum terms and two time-scales are involved. Furthermore, it is demonstrated that the designed distributed algorithms attain linear speedup convergence rates provided that the momentum coefficients and the step size are appropriately selected. Finally, numerical simulations verify the effectiveness and the global accelerated effect of the designed algorithms.

关键词： optimization Convergence Topology Network topology Eigenvalues and eigenfunctions Convex functions Numerical simulation Consensus directed interaction topology distributed constrained optimization gradient tracking linear convergence rate

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：