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IEEE Conference on Decision and Control

作者： Jinming Xu Ye Tian Ying Sun Gesualdo Scutari College of Control Science and Engineering Zhejiang University Hangzhou China School of Industrial Engineering Purdue University West-Lafayette IN USA

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

We study distributed composite optimization over networks: agents minimize a sum of smooth (strongly) convex functions-the agents' sum-utility-plus a nonsmooth (extended-valued) convex one. We propose a general unified algorithmic framework for such a class of problems, whose unified convergence analysis leverages the theory of operator splitting. Distinguishing features of our scheme are: (i) When the agents' functions are strongly convex, the algorithm converges at a linear rate, whose dependence on the agents' functions and network topology is decoupled, matching the typical rates of centralized optimization; the rate expression improves on existing results; (ii) When the objective function is convex (but not strongly convex), similar separation as in (i) is established for the coefficient of the proved sublinear rate; and (iii) A by-product of our analysis is a tuning recommendation for several existing (non-accelerated) distributed algorithms yielding the fastest provable (worst-case) convergence rate. This is the first time that a general distributed algorithmic framework applicable to composite optimization enjoys all such properties.

关键词： Convergence distributed algorithms Optimization Tuning Heuristic algorithms Standards Radio frequency

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A Generalized Nash Equilibrium Approach for Autonomous Energy Management of Residential Energy Hubs

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2019年第11期15卷 5892-5905页

作者： Liang, Yile Wei, Wei Wang, Cheng Alibaba Grp Beijing 100102 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China

The development of the cutting-edge technologies in cogeneration and trigeneration has led to a rapid transition toward integrated energy systems and the mushrooming of energy hubs, calling for effective energy management schemes. This paper proposes a distributed algorithm for autonomous energy management (AEM) of a cluster of residential energy hubs. Given the interactive behaviors of energy purchasing at the supply side, we treat each hub as a self-interested agent, and formulate the AEM problem of these hubs as a monotone generalized Nash game (MON-GNG). On one hand, there are global coupling constraints representing the supply limits of the input energy systems, which are imposed by the limited capacities of electrical feeders and natural gas pipelines, thus making it a GNG. On the other hand, the cost function of each hub is merely convex in its actions considering the input-to-output energy transformation inside and the impacts of the energy storage devices, which leads to an MON game. The existence of the generalized Nash equilibria (GNEs) of this MON-GNG can be theoretically guaranteed. Then, by reformulating the MON-GNG as a variational inequality problem with special decomposition structure, an efficient and single-loop distributed algorithm is then proposed for computing a GNE with clear economic interpretation based on an improved Tikhonov regularization technique. Principles of parameter selection that will guarantee convergence are suggested. Numeric simulations validate the convergence performance and effectiveness of the proposed algorithm.

关键词： distributed algorithms Games Energy management Resistance heating Turbines Optimization Nash equilibrium distributed algorithm energy hub generalized Nash equilibrium (GNE) integrated energy system (IES) variational inequality (VI)

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A distributed algorithm for vertex coloring problems in wireless networks

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ARRAY 2020年 6卷

作者： Miri, Mohammadhasan Darmani, Yousef Mohamedpour, Kamal Tummala, R. Lal Sarkar, Mahasweta Golestan Univ Dept ECE Gorgan Iran K N Toosi Univ Technol Dept ECE Tehran Iran San Diego State Univ Dept ECE San Diego CA USA

A lot of problems in computer networks are modeled by the vertex coloring problem (VCP), the multicoloring problem (MCP), the bandwidth coloring problem (BCP), and the bandwidth multicoloring problem (BMCP). To solve the VCP with A + 1 colors, a myriad distributed algorithms have been proposed to reduce time complexity (the number of rounds), where A is the maximum vertex degree in the graph. Time and communication complexities of these algorithms are functions of n and A, where n is the number of vertices in the graph. The MCP can be converted into VCP after transformation of the graph;the transformation increases the time and communication complexities. However, no distributed algorithms for BCP and BMCP have been proposed in the literature. In this paper, we propose a General distributed Vertex Coloring Algorithm (GDVCA) to solve four problems in wireless networks. In GDVCA, some (and not necessarily all) vertices assign colors to themselves and their neighbors, which we refer to them as assigning vertices. The communication cost of GDVCA is very low;the number of messages transmitted by each assigning vertex is one and by the other vertices is zero. Assigning vertices assign the first available colors to uncolored vertices;moreover assigning vertices can use heuristic methods to choose the next proper vertex for coloring. Therefore, the number of colors used by the distributed algorithm GDVCA is significantly low. The number of time slots required to color a graph is O(A2) and at most n, where each round comprises several time slots.

关键词： Bit complexity distributed algorithms Media access control Time complexity Vertex coloring problems Wireless networks

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A Communication-efficient Linearly Convergent Algorithm with Variance Reduction for distributed Stochastic Optimization

A Communication-efficient Linearly Convergent Algorithm with...

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European Control Conference (ECC)

作者： Jinlong Lei Peng Yi Jie Chen Yiguang Hong The Department of Control Science and Engineering Tongji University Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

This paper considers a distributed stochastic strongly convex optimization, where agents over a network aim to cooperatively minimize the average of all agents' local cost functions. Due to the stochasticity of gradient estimation and distributedness of local objective, fast linearly convergent distributed algorithms have not been achieved yet. This work proposes a novel distributed stochastic gradient tracking algorithm with variance reduction, where the local gradients are estimated by an increasing batch-size of sampled gradients. With an undirected connected communication graph and a geometrically increasing batch-size, the iterates are shown to converge in mean to the optimal solution at a geometric rate (achieving linear convergence). The iteration, communication, and oracle complexity for obtaining an c-optimal solution are established as well. Particulary, the communication complexity is O(ln(1/ε)) while the oracle complexity (number of sampled gradients) is O(1/ε 2 ), which is of the same order as that of centralized approaches. Hence, the proposed scheme is communication-efficient without requiring extra sampled gradients. Numerical simulations are given to demonstrate the theoretic results.

关键词： Convergence Complexity theory Cost function Estimation STEM distributed algorithms

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distributed Optimization of Second-Order nonlinear Multi-Agent Systems Subject to Communication Delays

Distributed Optimization of Second-Order nonlinear Multi-Age...

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Chinese Control Conference (CCC)

作者： Shiling Li Xiaohong Nian Zhenhua Deng Zhao Chen School of Automation Central South University Changsha P. R. China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, the distributed optimization problem of continuous-time second-order nonlinear multi-agent systems with communication delays under undirected graphs is studied. First, we show that the equilibrium point of the multi-agent systems with time delays is corresponding to the optimal solution of the problem. Next, based on gradient descent, we propose a distributed optimization algorithm. Besides, we analyze the global convergence of the algorithm by constructing a suitable Lyapunov function and linear matrix inequality. Finally, an example is presented to illustrate the effectiveness of our result.

关键词： Optimization Delays Multi-agent systems Delay effects distributed algorithms Convergence Linear matrix inequalities

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distributed Nash Equilibrium Computation for Mixed-order Multi-player Games

Distributed Nash Equilibrium Computation for Mixed-order Mul...

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International Conference on Control and Automation (ICCA)

作者： Jizhao Yin Maojiao Ye School of Automation Nanjing University of Science and Technology P.R. China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

Noticing that agents with different dynamics may work together, this paper considers Nash equilibrium computation for a class of games in which first-order integrator-type players and second-order integrator-type players interact in a distributed network. To deal with this situation, we firstly exploit a centralized method for full information games. In the considered scenario, the players can employ its own gradient information, though it may rely on all players' actions. Based on the proposed centralized algorithm, we further develop a distributed counterpart. Different from the centralized one, the players are assumed to have limited access into the other players' actions. Appropriate Lyapunov functions are constructed to investigate the effectiveness of the proposed methods analytically. It is shown that the proposed method would drive the players' actions to the Nash equilibrium exponentially. Lastly, the theoretical results are numerically verified by simulation examples.

关键词： Games Nash equilibrium Multi-agent systems Vehicle dynamics distributed algorithms Writing Symmetric matrices

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Differential Privacy for distributed Consensus with Partial Observations

Differential Privacy for Distributed Consensus with Partial ...

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International Conference on Control and Automation (ICCA)

作者： Ye Pu the University of Melbourne Australia

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

In a network, distributed privacy-preserving algorithms protect sensitive information by adding noise to the message before it is transmitted to other nodes in the network. Most existing works on differentially private distributed algorithm assumed that the adversary has access to all messages transmitted through the network. However, this is not a realistic assumption when dealing with real-world applications with large networks such as power networks. Very often the adversary can only breach a small part of the network and has limited observation of the messages. In this paper, we study how partial observations affect the privacy guarantee of a distributed consensus algorithm. For the distributed consensus problem, we show that observing different (set of) messages in the network could lead to very different privacy guarantee of the distributed algorithm.

关键词： Privacy Differential privacy Consensus algorithm Symmetric matrices Random variables distributed algorithms Routing

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distributed Formation Algorithm Based on Integrated Localization and Control

Distributed Formation Algorithm Based on Integrated Localiza...

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GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Kai Ma Yuan Shen Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

Collaborative formation is an important basis for multi-agent collaborative systems to complete advanced tasks. Previous studies show that the integrated framework of localization and control could achieve higher accuracy than the decoupled framework. We follow the integrated framework and design a feasible distributed formation algorithm based on relative measurements, which has near-optimal performance. Besides, we prove that if the measurement network topology is a connected regular graph, the proposed algorithm is asymptotically stable and an upper bound of the mean formation error is provided, which reveals how localization error and control error affect formation accuracy. Numerical results verify the performance of the proposed algorithm as well as the effects of the noise parameters and network topology on formation performance.

关键词： Location awareness Noise measurement distributed algorithms Upper bound Network topology Mathematical model Convergence

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ADMM-Based Sparse distributed Learning for Stochastic Configuration Networks

ADMM-Based Sparse Distributed Learning for Stochastic Config...

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Chinese Automation Congress (CAC)

作者： Yujun Zhou Wu Ai College of Science Guilin University of Technology Guilin China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Stochastic configuration networks (SCNs) are a new model of random neural networks (RNNs), which have demonstrated excellent capabilities in large-scale data analysis. For the processing of increasing data, distributed algorithms have become increasingly important. In this paper, the data sets are stored in multiple agents, and the centralized SCNs are reconstructed into equivalent sub-problems. The input weights and deviations are randomly generated under the constraints of supervised inequalities, ensuring the general approximation of the model, and proposed Based on the Alternating Direction Method of Multipliers (ADMM) distributed algorithm, L1 regularization is applied, sparse solutions are found, output weights are obtained, and the stochastic neural network model is improved. This method proves to be effective and greatly reduces the amount of information that needs to be exchanged between computing agents.

关键词： Training distributed algorithms Optimization Data models Approximation algorithms Stochastic processes distributed databases

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Scale-Free Vision-Based Aerial Control of a Ground Formation With Hybrid Topology

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2019年第4期27卷 1703-1711页

作者： Aranda, Miguel Mezouar, Youcef Lopez-Nicolas, Gonzalo Sagues, Carlos Univ Clermont Auvergne CNRS SIGMA Clermont Inst Pascal F-63000 Clermont Ferrand France Univ Zaragoza Inst Invest Ingn Aragon Zaragoza 50018 Spain

We present a novel vision-based control method to make a group of ground mobile robots achieve a specified formation shape with unspecified size. Our approach uses multiple aerial control units equipped with downward-facing cameras, each observing a partial subset of the multirobot team. The units compute the control commands from the ground robots' image projections, using neither calibration nor scene scale information, and transmit them to the robots. The control strategy relies on the calculation of image similarity transformations, and we show it to be asymptotically stable if the overlaps between the subsets of controlled robots satisfy certain conditions. The presence of the supervisory units, which coordinate their motions to guarantee a correct control performance, gives rise to a hybrid system topology. All in all, the proposed system provides relevant practical advantages in simplicity and flexibility. Within the problem of controlling a team shape, our contribution lies in addressing several simultaneous challenges: the controller needs only partial information of the robotic group, does not use distance measurements or global reference frames, is designed for unicycle agents, and can accommodate topology changes. We present illustrative simulation results.

关键词： distributed algorithms mobile robots multi-robot systems robot vision systems unmanned aerial vehicles

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