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distributed optimization With Personalization: A Flexible Algorithmic Framework

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第10期69卷 6715-6730页

作者： Huang, Yan Xu, Jinming Meng, Wenchao Wai, Hoi-To Chai, Li Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China

We consider a distributed personalized optimization problem over a network of nodes whose cost functions depend on a decision variable consisting of two parts: global (shared) part and local (node-specific) part. This problem structure arises in several important scenarios, where the global part captures the common feature among nodes and the local part represents the personalized feature. To solve this problem, we develop a flexible algorithmic framework employing personalized distributed stochastic gradient tracking method, where each node updates variables locally and communicates the shared part with its neighbors for coordination in a stochastic way. Different from existing works, the proposed framework provides great flexibility in designing algorithms with uncoordinated stepsizes for updating local and global parts over changing topologies. Most importantly, it can eliminate the effect of heterogeneity among nodes thanks to the gradient tracking scheme. Leveraging a properly designed Lyapunov function, we prove the convergence of the proposed algorithm for both (strongly) convex and nonconvex objective functions with cross-block Lipschitz gradients. The obtained rate results show clear dependence of the convergence performance on topology, heterogeneity of stepsize, and properties of objective functions. Numerical examples are provided to demonstrate the effectiveness of the proposed algorithm accounting for node-specific heterogeneity.

关键词： Peer-to-peer computing Convergence Stochastic processes Linear programming optimization Vectors Standards distributed optimization personalization uncoordinated update

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distributed optimization Framework for Mobile Data Collection With Energy Harvesting in Duty-Cycle WSNs

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IEEE INTERNET OF THINGS JOURNAL 2023年第7期10卷 6456-6473页

作者： Zhao, Duqiao Liu, Ding Cao, Xudong Xian Univ Technol Dept Control Sci & Control Engn Shaanxi Key Lab Complex Syst Control & Intelligent Xian 710048 Peoples R China

The increased dynamics and complexity of wireless sensor networks (WSNs) raise higher requirements on the flexibility and applicability of data collection algorithms. Therefore, a novel distributed optimization framework for data collection with energy harvesting in duty-cycle (DC) WSNs is developed in this article, which employs the mobile collector (called SenCar) to collect data from the selected sensors (called anchors) to circumvent the bottlenecks caused by the variations of energy distribution. In order to guarantee the optimality of mobile data collection, it is divided into two steps: first, a method for calculating the optimal number of anchors is developed and then an adaptive anchor selection algorithm is proposed to determine those anchors. Second, the mobile data collection problem of DC WSNs is formulated as a delay optimization problem constrained by flow conversation, congestion control, and energy balance. Then, we focus on designing the distributed algorithm to resolve that optimization problem, in which each sensor node only needs to communicate with its neighbors to make decisions without any global information and can locally adjust the information weight of its neighbors. Besides, the accelerated distributed algorithm with the uncoordinated step size is also proposed to improve the convergence rate. Furthermore, the explicit convergence analysis of the proposed algorithm is provided in this article. Finally, the numerical results show that our algorithm can quickly converge to the optimal solution and has obvious superiority in adjusting link flow, storing energy, and extending network lifetime.

关键词： Data collection Wireless sensor networks Energy harvesting Batteries optimization distributed databases Energy consumption distributed optimization duty-cycle (DC) energy harvesting mobile data collection wireless sensor networks (WSNs)

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distributed optimization of Second-Order Nonlinear Multiagent Systems With Event-Triggered Communication

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2021年第4期8卷 1954-1963页

作者： Li, Shiling Nian, Xiaohong Deng, Zhenhua Cent South Univ Sch Automat Changsha 410075 Hunan Peoples R China

In this article, we study the event-triggered distributed optimization problem of second-order nonlinear multiagent systems under undirected and connected communication topologies. To reduce the cost of information exchange in an optimization problem, a distributed event-triggered communication-based algorithm is proposed. In an event-triggered mechanism, the information interaction process of the network is controlled by a set of trigger conditions. The trigger condition of each agent only requires its own state information and its neighboring agents' state information. Each agent updates its own states only at the triggering time. In order to achieve the optimal result of the optimization problem and reduce the cost of communicatio, we propose a distributed event-triggered algorithm based on gradient descent. Moreover, we analyze the convergence of the algorithm by constructing a suitable Lyapunov function. Furthermore, we prove that the Zeno behavior can be avoid. Finally, we provide examples to validate the effectiveness of the obtained result.

关键词： distributed optimization event-triggered control nonlinear systems second-order systems zeno behavior

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distributed optimization for Multiagent Systems: An Edge-Based Fixed-Time Consensus Approach

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IEEE TRANSACTIONS ON CYBERNETICS 2019年第1期49卷 122-132页

作者： Ning, Boda Han, Qing-Long Zuo, Zongyu Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia Beihang Univ Seventh Res Div Beijing 100191 Peoples R China

This paper deals with the problem of distributed optimization for multiagent systems by using an edge-based fixed-time consensus approach. In the case of time-invariant cost functions, a new distributed protocol is proposed to achieve the state agreement in a fixed time while the sum of local convex functions known to individual agents is minimized. In the case of time-varying cost functions, based on the new distributed protocol in the case of time-invariant cost functions, a distributed protocol is provided by taking the Hessian matrix into account. In both cases, stability conditions are derived to ensure that the distributed optimization problem is solved under both fixed and switching communication topologies. A distinctive feature of the results in this paper is that an upper bound of settling time for consensus can be estimated without dependence on initial states of agents, and thus can be made arbitrarily small through adjusting system parameters. Therefore, the results in this paper can be applicable in an unknown environment such as drone rendezvous within a required time for military purpose while optimizing local objectives. Case studies of a power output agreement for battery packages are provided to demonstrate the effectiveness of the theoretical results.

关键词： distributed optimization fixed-time consensus multiagent systems

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distributed optimization for SWIPT-Enabled Hybrid-Powered Multicell Communication Networks With Energy Trading

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第12期23卷 19017-19033页

作者： Li, Guang-Ju Nie, Xiaokai Jin, Shi Liang, Le Yu, Wenwu Southeast Univ Sch Math Nanjing 210096 Peoples R China Southeast Univ Sch Automat Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing 210096 Peoples R China Southeast Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 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

This paper investigates a simultaneous wireless information and power transfer-enabled hybrid-powered multicell communication network with a nonlinear energy harvesting model. In this multicell environment, information interaction and energy trading are carried out among base stations (BSs), and each BS powered by hybrid sources simultaneously provides information/energy to its user equipments (UEs) over the downlinks. A novel global utility function is proposed by comprehensively considering the incomes from information and energy transmission, energy trading, and the costs from the electricity companies. To maximize this goal, a nonconvex problem that jointly optimizing energy procurement, power allocation, and power splitting ratios is formulated to deal with the imbalance between energy supply and demand at BSs. Considering the nonconvexity of the problem and the strong coupling among the optimization variables, the formulated problem is difficult to solve directly with conventional convex optimization methods. To overcome these obstacles, a two-step solution combining alternating optimization, distributed optimization, and successive convex approximation is designed, in which the BS layer scheme and the UE layer scheme are performed alternately. Different from the existing centralized schemes, the proposed distributed scheme makes local optimal decisions independently at each BS only resorting to the information of neighbor BSs, which brings great advantages in reducing signaling and computational overheads. Moreover, the convergence and advantages of the proposed distributed scheme are verified by rigorous analyses and simulations.

关键词： optimization Wireless communication Renewable energy sources Communication networks Energy consumption Costs Computer architecture Simultaneous wireless information and power transfer Resource management Network architecture SWIPT energy trading green communications distributed optimization

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distributed optimization With Coupling Constraints

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第3期68卷 1847-1854页

作者： Wu, Xuyang Wang, He Lu, Jie KTH Royal Inst Technol Div Decis & Control Syst SE-10044 Stockholm Sweden ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China

In this article, we investigate distributed convex opti-mization with both inequality and equality constraints, where the objective function can be a general nonsmooth convex function and all the constraints can be both sparsely and densely cou-pling. By strategically integrating ideas from primal-dual, proxi-mal, and virtual-queue optimization methods, we develop a novel distributed algorithm, referred to as IPLUX, to address the prob-lem over a connected, undirected graph. We show that IPLUX achieves an O(1/k) rate of convergence in terms of optimality and feasibility, which is stronger than the convergence results of the alternative methods and eliminates the standard assumption on the compactness of the feasible region. Finally, IPLUX exhibits faster convergence and higher efficiency than several state-of-the-art methods in the simulation.

关键词： Convergence Couplings optimization Convex functions Linear programming distributed algorithms Transforms Constrained optimization distributed optimization primal-dual method proximal algorithm

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distributed optimization for Disturbed Second-Order Multiagent Systems Based on Active Antidisturbance Control

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020年第6期31卷 2104-2117页

作者： Wang, Xiangyu Li, Shihua Wang, Guodong Southeast Univ Sch Automat Nanjing 210096 Peoples R China Minist Educ Key Lab Ind Internet Things & Networked Control Chongqing 400065 Peoples R China Southeast Univ Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing 210096 Peoples R China

In this article, the distributed optimization problem is studied for second-order multiagent systems with both mismatched and matched disturbances. For this problem, a distributed active antidisturbance control framework is established, which consists of both disturbance estimation/compensation and distributed feedforward-feedback composite control design. In the first stage, some disturbance estimators are utilized to estimate various types of matched/mismatched disturbances for each agent. In the second stage, for each agent, based on the disturbance estimates, the information exchanges between the neighboring agents, and the gradient of the local cost function only accessible to itself, a kind of distributed composite controllers are proposed. Under these controllers, all the agents' outputs asymptotically reach consensus to the minimizer of the global cost function, which is the sum of all the local cost functions. The closed-loop system convergence is proven based on a new Lyapunov function, convex analysis, and an input-to-state stability criterion. Simulations demonstrate the effectiveness of the proposed control scheme.

关键词： Multi-agent systems Cost function Convergence Closed loop systems Asymptotic stability Active antidisturbance control consensus distributed optimization mismatched disturbances multiagent systems

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distributed optimization problem for double-integrator systems with the presence of the exogenous disturbances

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NEUROCOMPUTING 2018年 272卷 386-395页

作者： Ngoc-Tu Tran Wang, Yan-Wu Yang, Wu Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Hubei Peoples R China China Three Gorges Univ Hubei Prov Collaborat Innovat Ctr New Energy Micr Yichang Peoples R China

The aim of this paper is to study the distributed optimization problem for continuous-time multi-agent systems with the existence and the interference of external disturbance, therein each agent is described as double-integrator dynamic. To reject the exogenous disturbance, the distributed algorithm is proposed for each agent based on the internal model principle. The proposed algorithm only utilizes the position information of each agent from its neighbors subject to the undirected graph, which can reduce communication costs and energy consumptions in applications. Moreover, the algorithm only needs the cost functions of the agent itself, which can greatly protect the privacy of other agents. The optimal solution of the problem is thus obtained with the design of Lyapunov function and the help of convex analysis, LaSallel's Invariance Principle. Finally, two numerical simulation examples and the comparison of proposed algorithm with other previous research are presented to illustrate the persuasive effectiveness of the theoretical result. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Second-order multi-agent systems distributed optimization Gradient-based algorithm External disturbance Internal model principle

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distributed optimization for the multi-robot system using a neurodynamic approach

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NEUROCOMPUTING 2019年 367卷 103-113页

作者： Fang, Xiaomeng Pang, Dong Xi, Juntong Le, Xinyi Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China Shanghai Key Lab Adv Mfg Environm Shanghai 200240 Peoples R China

In this paper, we use a connected undirected graph to describe the multiple redundant manipulator system. An optimization model is formulated as a convex problem with coupled constraints. These constraints include equality constraints derived for path tracking, inequality constraints derived for obstacle avoidance, and convex sets built for joint physical limits. A novel distributed neurodynamics-based algorithm is developed for solving the complex problem in real time, so that there is no need for having a center coordinator in the multi-robot system. To verify the established model and the proposed algorithm, a dual-robot system is simulated to carry a rigid object following desired trajectories with obstacles considered. A more complex tri-robot system is simulated to perform as a supplementary evidence. (C) 2019 Elsevier B.V. All rights reserved.

关键词： distributed optimization Multi-robot system Trajectory tracking Obstacle avoidance Recurrent neural networks

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distributed optimization Using Reduced Network Equivalents for Radial Power Distribution Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2021年第4期36卷 3645-3656页

作者： Sadnan, Rabayet Dubey, Anamika Washington State Univ Dept Sch Elect Engn & Comp Sci Pullman WA 99164 USA

The limitations of centralized optimization methods for power systems operation have led to the distributed computing paradigm, particularly in power distribution systems. The existing techniques reported in recent literature for solving distributed optimization problems are not viable for power distribution systems applications. The essential drawback remains a large number of required communication rounds, i.e., macro-iterations among the computing agents to solve one instance of the optimization problem;the typical number of macro-iterations are in the order of 10(2)similar to 10(3). In this paper, a new and scalable distributed optimization method based on Equivalent Network Approximation (ENApp) is proposed to solve optimal power flow (OPF) for a balanced radial distribution system. Specifically, the distribution system's radial topology is leveraged to reduce the decomposed systems into upstream and downstream network equivalents. The proposed innovations reduce the required number of macro-iterations/communication-rounds for convergence by order of magnitude. The approach is validated using IEEE 123-bus and IEEE 8500-node test systems and is shown to converge to the same optimal solution as obtained using an equivalent centralized OPF (C-OPF) model.

关键词： distributed Energy Resources (DERs) distributed optimization loss minimization optimal power flow

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