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An operator splitting approach for distributed generalized Nash equilibria computation

AUTOMATICA 2019年 102卷 111-121页

作者： Yi, Peng Pavel, Lacra Univ Toronto Dept Elect & Comp Engn 10 Kings Coll Rd Toronto ON M5S 3G4 Canada

In this paper, we propose a distributed algorithm for computation of a generalized Nash equilibrium (GNE) in noncooperative games over networks. We consider games in which the feasible decision sets of all players are coupled together by a globally shared affine constraint. Adopting the variational GNE as a refined solution, we reformulate the problem as that of finding the zeros of a sum of monotone operators through a primal-dual analysis and an augmentation of variables. Then we introduce a distributed algorithm based on forward-backward operator splitting methods. Each player only needs to know its local objective function, local feasible set, and a local block of the affine constraint, and share information with its neighbours. However, each player also needs to observe the decisions that its objective function directly depends on to evaluate its local gradient. We show convergence of the proposed algorithm for fixed step-sizes under some mild assumptions. Moreover, a distributed algorithm with inertia is also introduced and analysed for distributed variational GNE seeking. Numerical simulations are given for networked Cournot competition with bounded market capacities, to illustrate the algorithm efficiency and performance. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Generalized Nash equilibrium Multi-agent systems distributed algorithm Operator splitting

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Generalized Nash equilibrium seeking strategy for distributed nonsmooth multi-cluster game

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AUTOMATICA 2019年 103卷 20-26页

作者： Zeng, Xianlin Chen, Jie Liang, Shu Hong, Yiguang Beijing Inst Technol Sch Automat Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Minist Educ Key Lab Knowledge Automat Ind Proc Beijing 100083 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China

This paper investigates the distributed strategy design to find generalized Nash equilibria (GNE) of multi-cluster games with nonsmooth payoff functions, a coupled nonlinear inequality constraint, and set constraints. In this game, each cluster is composed of a group of agents and is a virtual noncooperative player, who minimizes its payoff function;each agent only uses its local payoff function, local feasible set and partial information of the coupled inequality constraint, and communicates with its neighbors. To solve the GNE problem, we propose a distributed nonsmooth algorithm using a projected differential inclusion and establish the convergence analysis of the proposed algorithm. A numerical application is given for illustration. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： distributed algorithm GNE seeking Nonsmooth function Multi-cluster game Set constraint Coupled nonlinear inequality constraint

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A multiagent system based optimal load restoration strategy in distribution systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 124卷 106314-106314页

作者： Yang, Lun Xu, Yinliang Sun, Hongbin Chow, Moyuen Zhou, Jianguo Tsinghua Univ Tsinghua Berkeley Shenzhen Inst TBSI Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

Natural disasters pose a significant impact on the distribution infrastructures and often cause severe power outage including critical services. This paper proposes a new restoration approach to restore critical loads using distributed energy resources (DERs). Specifically, the critical load restoration problem, considering both the availability of DERs and the optimal restoration path, is formulated as a variation of the shortest path problem. The objective aims at maximizing the reliability of the restoration plan to decrease the possibility of postrestoration failures. A distributed approach framework embedded min/max-consensus, and bias min-consensus algorithms is developed, which only requires information exchange among neighbors based on a peer-to-peer communication protocol. The proposed distributed restoration approach is adaptive to topology changes and robust to unreliable communication network. Numerical results with various scenarios demonstrate the effectiveness of the proposed distributed restoration approach.

关键词： distributed energy resources Load restoration distributed algorithm Reliability Shortest path problem

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A distributed Hybrid Event-Time-Driven Scheme for Optimization Over Sensor Networks

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2019年第9期66卷 7199-7208页

作者： Hu, Bin Guan, Zhi-Hong Chen, Guanrong Shen, Xuemin Huazhong Univ Sci & Technol Britton Chance Ctr Biomed Photon Minist Educ Wuhan Natl Lab Optoelect Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Biomed Photon Sch Engn Sci Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Coll Automat Wuhan 430074 Hubei Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

In sensor networks (SNs), how to allocate the resources so as to optimize data gathering and network utility is an important and challenging task. This paper studies the distributed optimization problem in SNs. A distributed hybrid-driven algorithm based on the coordinate descent method is presented for the optimization purpose. The proposed optimization algorithm differs from the existing ones since the hybrid driven scheme allows more choices of actuation time, resulting a tradeoff between communications and computation performance. Applying the proposed algorithm, each sensor node is driven in a hybrid event time manner, which removes the requirement of strict time synchronization. The convergence and optimality of the proposed algorithm are analyzed, and then verified by simulation examples. The developed results also show the tradeoff between communications and computation performance.

关键词： distributed algorithm hybrid event-time-driven scheme optimization sensor network (SN)

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Estimate exchange over network is good for distributed hard thresholding pursuit

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SIGNAL PROCESSING 2019年 156卷 1-11页

作者： Zaki, Ahmed Mitra, Partha P. Rasmussen, Lars K. Chatterjee, Saikat KTH Royal Inst Technol Sch Elect Engn Stockholm Sweden Cold Spring Harbor Lab 1 Bungtown Rd New York NY USA

We investigate an existing distributed algorithm for learning sparse signals or data over networks. The algorithm is iterative and exchanges intermediate estimates of a sparse signal over a network. This learning strategy using exchange of intermediate estimates over the network requires a limited communication overhead for information transmission. Our objective in this article is to show that the strategy is good for learning in spite of limited communication. In pursuit of this objective, we first provide a restricted isometry property (RIP)-based theoretical analysis on convergence of the iterative algorithm. Then, using simulations, we show that the algorithm provides competitive performance in learning sparse signals vis-a-vis an existing alternate distributed algorithm. The alternate distributed algorithm exchanges more information including observations and system parameters. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Sparse learning distributed algorithm Greedy pursuit algorithm RIP analysis

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An efficient and social-aware distributed in-network caching scheme in named data networks using matching theory

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COMPUTER NETWORKS 2019年第0期158卷 175-183页

作者： Ehsanpour, Mahsa Bayat, Siavash Hemmatyar, Ali Mohammad Afshin Sharif Univ Technol Comp Engn Dept Tehran Iran Sharif Univ Technol Elect Res Inst Tehran Iran

One of the main characteristics of Named Data Networks (NDN) which makes it a promising architecture for the future of the Internet is its universal in-network caching. Due to the limited caching capacity of the network and the infinite amount of data objects, the cache management scheme becomes a challenging task in NDN. While existing cache management schemes utilize network's caching capacity inefficiently by redundantly keeping popular objects across the network and concentrate on improving bandwidth utilization, we focus on a novel criterion to elevate the cache utilization efficiency. In this paper, a distributed solution that associates the objects to the cache-enabled nodes and maximizes the total network's social welfare is proposed which models the problem of network's social welfare maximization as a many-to-one matching game. Further, it is proved that the proposed solution has a number of desired properties. The resulting matching is stable and the outcome of the proposed algorithm is in Competitive Equilibrium, the social-welfare of the proposed algorithm converges to the social-welfare of the centralized optimal approach as the price step number is sufficiently small, the proposed algorithm converges after a finite number of iterations with relatively low overhead and the cost of retrieving the required objects is the minimum for the Internet Service Providers (ISP). Finally, simulation results show the convergence of our proposed scheme to the centralized optimal solution and its outperformance against the default applied caching scheme in NDN in terms of both network's social welfare and hit ratio. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Named Data Networks Cache management scheme Matching theory distributed algorithm

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distributed Stochastic Approximation algorithm for Time-Varying Regression Function Over Network 37

Distributed Stochastic Approximation Algorithm for Time-Vary...

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37th Chinese Control Conference (CCC)

作者： Fu, Kewei Chen, Han-Fu Zhao, Wenxiao 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

ISBN: (纸本)9789881563958

In this paper we consider the distributed root-tracking problem for a sum of time-varying regression functions over a network, where each agent only has local observation and aims at finding the root of the global regression function. Since the regression functions are time-varying, we have to track the changing roots. A distributed stochastic apprximation algorithm is introducted for solving the problem and that the consensus and convergence of the estimates are proved.

关键词： Stochastic approximation distributed algorithm multi-agent system time-varying regression function

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distributed networked localization using neighboring distances only through a computational topology control approach

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INTERNATIONAL JOURNAL OF distributed SENSOR NETWORKS 2020年第3期16卷 1-10页

作者： Wang, Xiaochu Sun, Ting Sun, Changhao Wang, Junqi China Acad Space Technol Qian Xuesen Lab Space Technol Beijing 100080 Peoples R China Beijing Informat Sci & Technol Univ Sch Photoelect Informat & Commun Engn Beijing Peoples R China Shanghai Acad Social Sci Inst World Econ Shanghai Peoples R China

For large-scale wireless sensor networks, the nonlinear localization problem where only neighboring distances are available to each individual sensor nodes have been attracting great research attention. In general, distributed algorithms for this problem are likely to suffer from the failures that localizations are trapped in local minima. Focusing on this issue, this article considers a fully distributed algorithm by introducing a novel mechanism, where each individual node is allowed to computationally interact with a random subset of its neighbors, for helping localizations escape from local minima. Theoretical analyses reveal that with the proposed algorithm, any local minimum of the localization will be unstable, and the global optimum would finally be achieved with probability 1 after enough time of iterations. Numerical simulations are given as well to demonstrate the effectiveness of the algorithm.

关键词： Node localization distributed algorithm global optimum sensor networks topology control

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Decentralized adaptive resource-aware computation offloading & caching for multi-access edge computing networks

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2021年 30卷 100555-100555页

作者： Tefera, Getenet She, Kun Shelke, Maya Ahmed, Awais Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Sichuan Peoples R China Symbiosis Int Univ Dept Comp Sci & Informat Technol Pune Maharashtra India

Decentralized computation offloading and caching in Multi-Access Edge Computing (MEC) is a promising approach to evolve the forthcoming network generation. MEC is the emerging technology that provides adaptive micro cloud services to the edge of proximity resource-constrained smart communication and Internet of Everything (IoE) devices for cellular subscribers. Nowadays, Massive IoE devices are exponentially connected to the global ecosystem. As a result, the backhaul network traffic grows enormously and users' ultra-reliable low latency communications are challenging as well. In this paper, we explored decentralized adaptive resourceaware communication, computing, & caching framework which can orchestrate the dynamic network environments based on Deep Reinforcement Learning (DRL). Subsequently, the framework can perform augmented decision-making capabilities to enhance users' connectivity and resource utilization requirements. Basically, every IoE device user are attempting to capitalize their own utilities. Hence, the problem is formulated using Non-cooperative game theory which is non-deterministic polynomial to solve the structural property of the MEC networks. We analyze and show that the game admits a Nash Equilibrium. Moreover, we have introduced a decentralized cognitive scheduling algorithm by exploiting DRL technology to leverage the utility of IoE & smart communication devices. Therefore, numerical results and theoretical analysis revealed that the proposed algorithm outperform, ultra-reliable low latency, and scalable than the baseline schemes.

关键词： Multi-access edge computing Cloud computing distributed computation offloading Radio-access networks Game theory Deep reinforcement learning distributed algorithm

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Cooperation-Based distributed Economic MPC for Economic Load Dispatch and Load Frequency Control of Interconnected Power Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2019年第5期34卷 3964-3966页

作者： Jia, Yubin Dong, Zhao Yang Sun, Changyin Meng, Ke Southeast Univ Minist Educ Sch Automat Nanjing 210096 Jiangsu Peoples R China Southeast Univ Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing 210096 Jiangsu Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2033 Australia

In this letter, a distributed economic model predictive control (EMPC) strategy is proposed for economic load dispatch and load frequency control of interconnected power systems. A general economic cost function is used in the EMPC to calculate the control sequence directly optimizes the economic performance of the system without using a hierarchical control structure, thus achieving both the real-time optimization and transient optimization. A cooperation-based distributed EMP Calgorithm is proposed and the asymptotic stability is proven. Simulation results reveal the advantages of the proposed method.

关键词： Economic load dispatch load frequency control economic model predictive control distributed algorithm cooperation-based

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