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distributed Algorithm Design for Nonsmooth and Nonlinear Resource Allocation Problems of Autonomous High-Order Agents and Its Application to Smart Grids

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第12期71卷 16473-16483页

作者： Deng, Zhenhua Liu, Cong Cent South Univ Sch Automat Changsha 410075 Peoples R China

This article investigates nonsmooth resource allocation problems (RAPs) of autonomous agents, in which the agents have high-order dynamics. Moreover, each agent has a nondifferentiable and private cost function, and the decisions of all agents are restricted by nonlinear network resource constraints and local nonlinear constraints. To the best of our knowledge, there have been no nonsmooth RAPs of high-order agents emerging, much less with nonlinear constraints. Besides, owing to the high-order dynamics, the nonsmooth cost functions and/or the nonlinear constraints, existing distributed algorithms for RAPs are infeasible for our problem. For the purpose of controlling high-order agents to execute nonsmooth resource allocation tasks autonomously, we propose a fully distributed algorithm by means of primal-dual methods and state feedback. In the fully distributed approach, all agents update their control inputs only on the basis of their own and neighbors' information. Further, we prove the global convergence of the algorithm via nonsmooth analysis and set-valued Lasalle invariance principle. Lastly, we apply the proposed algorithm to the economic dispatch problems (EDPs) of smart grids. By means of our algorithm, the turbine generators can autonomously perform the economic dispatch tasks.

关键词： Cyber-physical systems distributed algorithms high-order multiagent systems nonlinear constraints resource allocation

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A distributed Feedback-Based Framework for Nonlinear Aggregative Optimal Control

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第6期70卷 3784-3799页

作者： Sforni, Lorenzo Carnevale, Guido Notarstefano, Giuseppe Alma Mater Studiorum Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy

In this article, we propose a distributed, first-order, feedback-based approach to solve nonlinear optimal control problems with aggregative cost functions over networks of cooperative multiagent systems. Taking inspiration from a centralized, first-order optimal control framework, named GoPRONTO, we propose a distributed method exploiting a feedback scheme iteratively updated according to a distributed tracking mechanism. Due to the aggregative structure of the problem and the desired distributed paradigm, the centralized scheme would require global quantities that are not locally available. Thus, our distributed method concurrently updates a proxy of the centralized scheme with a set of local, auxiliary variables named trackers which suitably exploit interagent communication to reconstruct the global quantities. By relying on LaSalle-based arguments, we theoretically prove that our algorithm generates a sequence of trajectories converging to the set of trajectories satisfying the first-order necessary conditions for optimality. Finally, we corroborate the theoretical results with numerical simulations on a distributed optimal control application for a fleet of 50 quadrotors.

关键词： Optimal control Trajectory Costs Vehicle dynamics distributed algorithms Cost function Vectors Surveys Multi-agent systems Games distributed optimization multiagent systems optimal control optimization algorithms

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Dynamic Regret for distributed Online Composite Optimization

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第5期70卷 3056-3071页

作者： Hou, Ruijie Yu, Yang Li, Xiuxian Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Shanghai 201800 Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Frontiers Sci Ctr Intelligent Autonomous Syst Natl Key Lab Autonomous Intelligent Unmanned Syst Shanghai 201210 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 201210 Peoples R China

This article focuses on online composite optimization over multiagent networks. In the distributed setting, each agent has its own local loss function, which consists of a convex, strongly convex or strongly convex and smooth function, and a time-varying nonsmooth regularizer. Two distributed online algorithms are proposed and corresponding dynamic regrets are analyzed. Two proposed algorithms are based on signs of relative states. The first algorithm obtains O(root T(C-T + 1)) dynamic regret bound when each local loss is a general convex composite function, where C-T is the path variation. If C-T can be estimated in advance for convex or strongly convex local loss with a time-varying nonsmooth regularizer, then dynamic regret bounds are, respectively, in the order of O(root T(C-T + 1)) and O(logT(1 + C-T)). The second algorithm is based on the first one, especially for handling the local loss composed of a strongly convex and smooth function with a nonsmooth regularizer, and then obtains O(1 + C-T) dynamic regret bound. In the end, numerical results are given to support the theoretical findings.

关键词： Heuristic algorithms Optimization Power system dynamics Approximation algorithms Vectors Noise Linear programming Convex functions Benchmark testing Loss measurement Composite optimization distributed algorithms dynamic regret online optimization proximal gradient sign information

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Simple and Optimal algorithms for Heavy Hitters and Frequency Moments in distributed Models 25

Simple and Optimal Algorithms for Heavy Hitters and Frequenc...

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Proceedings of the 57th Annual ACM Symposium on Theory of Computing

作者： Zengfeng Huang Zhongzheng Xiong Xiaoyi Zhu Zhewei Wei Fudan University Shanghai China Renmin University of China Beijing China

ISBN: (纸本)9798400715105

We consider the problems of distributed heavy hitters and frequency moments in both the coordinator model and the distributed tracking model (also known as the distributed functional monitoring model). We present simple and optimal (up to logarithmic factors) algorithms for ℓp heavy hitters and Fp estimation (p ≥ 2) in these distributed *** ℓp heavy hitters in the coordinator model, our algorithm requires only one round and uses Õ(kp−1/p) bits of communication. For p > 2, this is the first near-optimal result. By combining our algorithm with the standard recursive sketching technique, we obtain a near-optimal two-round algorithm for Fp in the coordinator model, matching a significant result from recent work by Esfandiari et al. (STOC 2024). Our algorithm and analysis are much simpler and have better costs with respect to logarithmic factors. Furthermore, our technique provides a one-round algorithm for Fp, which is a significant improvement over a result of Woodruff and Zhang (STOC 2012).Thanks to the simplicity of our heavy hitter algorithms, we manage to adapt them to the distributed tracking model with only a (n) increase in communication. For ℓp heavy hitters, our algorithm has a communication cost of Õ(kp−1/p), representing the first near-optimal algorithm for all p ≥ 2. By applying the recursive sketching technique, we also provide the first near-optimal algorithm for Fp in the distributed tracking model, with a communication cost of Õ(kp−1/2) for all p ≥ 2. Even for F2, our result improves upon the bounds established by Cormode, Muthukrishnan, and Yi (SODA 2008) and Woodruff and Zhang (STOC 2012), nearly matching the existing lower bound for the first time.

关键词： distributed algorithms

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Discrete-Time distributed Least Square algorithms for Linear Equations via Layered Coordination

Discrete-Time Distributed Least Square Algorithms for Linear...

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Chinese Control and Decision Conference, CCDC

作者： Bo Wang Jinghao Li College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

This paper is concerned with the problem of designing discrete-time distributed algorithms for the least squares solution of over-determined linear equations via layered coordination. A row-column partition and a column-row partition are provided to partition the augmented matrix associated with the linear equations into several block matrices, respectively. By assigning an equation solver layer to computation and a data integration layer to data exchanges, a row-column partition-based discrete-time distributed algorithm and a column-row partition-based discrete-time distributed algorithm are proposed for solving over-determined linear equations, respectively. It is shown that the distributed algorithms proposed can reach a consensus exponentially on the least squares solution of linear equations. Finally, the effectiveness of the distributed algorithms proposed is validated via a numerical simulation.

关键词： distributed databases Data integration Numerical simulation Mathematical models Partitioning algorithms distributed algorithms

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distributed Nash Equilibrium Seeking for Multi-Cluster Game of Second-Order Nonlinear Systems With Partial Information

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第1期71卷 226-230页

作者： Nian, Xiaohong Li, Fan Liu, Dongxin Cent South Univ Sch Automat Key Lab Inst Cluster Unmanned Syst Changsha 410083 Peoples R China

In this brief, the multi-cluster game of unknown second-order nonlinear systems with partial information is investigated. Therein, each cluster consisting of several collaborating agents selfishly minimizes its own cost function with each other. The agents within each cluster are constrained by coupled inequality constraints and the local cost functions depend on the global decisions of all game participants. With the help of the state feedback, a novel distributed algorithm is designed by a synthesis of the primal-dual dynamic and the leader-following consensus protocol. Moreover, the convergence analysis is conducted by the Lyapunov stability theory, and it is proved that the algorithm can converge to the Nash equilibrium (NE) of the multi-cluster game. Finally, the effectiveness of the algorithm is verified by numerical simulation.

关键词： Multi-cluster game distributed algorithms coupled inequallty constraints partial-decision information second-order nonlinear system

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Penalty Removal Search Algorithm for distributed Optimization of Nonconvex Functions in Economic Dispatch

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年

作者： Ge, Yiyang Wang, Zhanshan Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China State Key Lab Synthet Automation Proc Ind Shenyang 110819 Peoples R China

Nonconvexity is a usually overlooked factor in economic dispatch (ED). Enhancing the nonconvexity of the objective function leads traditional convex optimization algorithms easily to fall into the local optimum. To address the above problem, a penalty removal search algorithm (PRSA) is proposed for ED nonconvex optimization. It is composed of two distributed optimization algorithms embedded in a reinforcement learning framework. In Phase I of PRSA, a distributed optimization algorithm with projection operators is designed. It uses fewer variables to locate the region where the optimal solution belongs by the cooperative Q-learning. In Phase II of PRSA, the sigmoid function serves as a penalty function to form the second distributed optimization algorithm. This is used to skip the searched solutions and allow the algorithm to continue searching for more feasible solutions. The PRSA solves the problem that the algorithm misses feasible solutions when the nonconvex coefficients increase. Finally, the effectiveness of the PRSA is verified by numerical examples.

关键词： Linear programming Economics distributed algorithms Cost function Q-learning Germanium Costs Valves Fuels Convex functions Collaborative Q-learning (CQL) algorithm distributed optimization economic dispatch (ED) nonconvex optimization penalty removal search algorithm (PRSA)

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distributed Optimal Contract for Data Rewarding with Network Effects

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IEEE Transactions on Control of Network Systems 2024年第2期12卷 1332-1341页

作者： Baneshi, Alireza Montazeri, Mina Kebriaei, Hamed University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran Swiss Federal Laboratories for Materials Science and Technology Urban Energy Systems Laboratory Dübendorf Switzerland School of Computer Science Tehran Iran

Data rewarding is a novel business model that leads to an economic trend in mobile networks. In this scheme, the advertiser incentivizes mobile users (MUs) to watch advertisement (ads) and, in return, receive a reward in the form of mobile data. In this work, we model the interaction between an advertiser who has asymmetric information about MUs and MUs who are connected to each other under a network, using the contract theory approach. We obtain the necessary and sufficient conditions for an optimal and practical contract to motivate MUs to participate in the data rewarding scheme and encourage them to declare their private information truthfully. The formulation of this contract is a non-convex-constrained optimization problem. Using lemmas and propositions, we reformulate the initial optimization problem that is challenging to solve as an optimization problem with convex constraints and prove that these two problems are equivalent. Then, with the help of a distributed and non-convex algorithm, we obtain the amount of ads demand and incentive reward. © 2014 IEEE.

关键词： Contracts Optimization Social networking (online) Data models Network systems Control systems Indexes distributed algorithms Couplings Complexity theory

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A Cutting Plane-Based distributed Algorithm for Non-Smooth Optimisation With Coupling Constraints

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 1223-1228页

作者： Zhong, Tianyi Angeli, David Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England Univ Florence Dept Informat Engn I-50121 Florence Italy

In this letter, we study a general setup for constrained convex optimisation over time-varying networks. We propose a distributed algorithm, based on the cutting plane method, to address non-smooth optimisation challenges. Cutting plane-based approaches require constraint consensus which is structurally different from established consensus schemes. We bridge this gap by linking the cutting plane-based algorithm with a dynamic average tracking scheme. The distributed cutting plane algorithm is presented and its convergence is analysed. Its performance is investigated through a numerical example.

关键词： Optimization Heuristic algorithms Approximation algorithms Linear approximation Estimation distributed algorithms Couplings Cutting plane non-smooth optimisation dynamic average tracking distributed algorithm

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Plug-and-Play distributed algorithms for Optimized Power Generation in a Microgrid

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IEEE TRANSACTIONS ON SMART GRID 2014年第4期5卷 2145-2154页

作者： Crisostomi, Emanuele Liu, Mingming Raugi, Marco Shorten, Robert Univ Pisa Dept Energy Syst Terr & Construct Engn I-56126 Pisa Italy Natl Univ Ireland Hamilton Inst Maynooth Kildare Ireland IBM Res Ireland Dublin 4 Ireland

This paper introduces distributed algorithms that share the power generation task in an optimized fashion among the several distributed Energy Resources (DERs) within a microgrid. We borrow certain concepts from communication network theory, namely Additive-Increase-Multiplicative-Decrease (AIMD) algorithms, which are known to be convenient in terms of communication requirements and network efficiency. We adapt the synchronized version of AIMD to minimize a cost utility function of interest in the framework of smart grids. We then implement the AIMD utility optimisation strategies in a realistic power network simulation in Matlab-OpenDSS environment, and we show that the performance is very close to the full-communication centralized case.

关键词： distributed algorithms microgrid optimal power scheduling

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