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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Wenjie Fan Mingzhen Jia Zhitong Liu State Grid Liaoning Marketing Service Center Shenyang China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

The Spark distributed algorithm is a distributed computing implementation based on the map reduce algorithm, which has the advantages of Hadoop MapReduce. However, unlike MapReduce, the intermediate output and results of jobs can be saved in memory, eliminating the need to read and write HDFS. Therefore, Spark is better suited for iterative map reduce algorithms such as data mining and machine learning. Spark distributed algorithm has been widely used in the Internet, advertising, finance and other industries, but less in the power industry. At present, the research on market power risk of power generators has been very in-depth, but there is little research on simulation platform algorithms for identifying market power risk. This article proposes a market power identification platform for power generation enterprises based on the Spark distributed framework, which can effectively accelerate the calculation speed of market power indicators. Furthermore, using a certain regional power grid as a simulation example, the monopoly situation of various power generation enterprises and the entire wholesale market is analyzed, proving the effectiveness of the Spark distributed framework algorithm. As the number of grid nodes increases, the advantages of the distributed algorithm will become more prominent.

关键词： Machine learning algorithms Machine learning Power grids Power industry Iterative algorithms Monopoly Sparks Internet of Things distributed algorithms Power generation

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distributed Optimization With Event-Triggered Communication via Input Feedforward Passivity

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IEEE CONTROL SYSTEMS LETTERS 2021年第1期5卷 283-288页

作者： Li, Mengmou Su, Lanlan Liu, Tao Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Univ Leicester Sch Engn Leicester LE1 7RH Leics England

In this letter, we address the distributed optimization problem with event-triggered communication by the notion of input feedforward passivity (IFP). First, we analyze the distributed continuous-time algorithm over uniformly jointly strongly connected balanced digraphs in an IFP-based framework. Then, we propose a distributed event-triggered communication mechanism for this algorithm. Next, we discretize the continuous-time algorithm by the forward Euler method with a constant stepsize irrelevant to network size, and show that the discretization can be seen as a stepsize-dependent passivity degradation of the input feedforward passivity. Thus, the discretized system preserves the IFP property and enables the same event-triggered communication mechanism but without Zeno behavior due to the discrete-time nature. Finally, a numerical example is presented to illustrate our results.

关键词： Optimization Feedforward systems Communication networks Radio frequency Heuristic algorithms Indexes distributed algorithms distributed optimization input feedforward passivity event-triggered control

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distributed Nash Equilibrium Seeking of Second-Order Multi-Agent Systems Under Unknown False Data Injection Attacks

Distributed Nash Equilibrium Seeking of Second-Order Multi-A...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Zhi Feng Guoqiang Hu Xiwang Dong Jinhu Lu School of Automation Science and Electrical Engineering Beihang University Beijing P. R. China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

This paper investigates resilient adaptively distributed Nash Equilibrium (NE) seeking problems for noncooperative games of double-integrator players under malicious attacks, i.e., false data injection (FDI) attacks. Different from distributed NE seeking in many existing literature under ideal environments, it is challenging to achieve the NE seeking under unknown and unbounded FDI attacks. An attack-resilient NE seeking architecture that is distributed, is established by means of the consensus-based gradient play together with an attack identifier to deal with the impacts of FDI attacks. Another key characteristic is that malicious FDI attacks are allowed to be completely unknown and unbounded. The global asymptotic convergence of the proposed distributed NE seeking algorithm to-ward the NE is guaranteed. Lastly, numerical simulation results are provided to illustrate the effectiveness of the proposed algorithms.

关键词： distributed databases Games Nash equilibrium Numerical simulation Trajectory distributed algorithms Artificial intelligence Multi-agent systems Convergence Lyapunov methods

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Brief Announcement: Towards Optimal distributed Delta Coloring 25

Brief Announcement: Towards Optimal Distributed Delta Colori...

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Proceedings of the ACM Symposium on Principles of distributed Computing

作者： Manuel Jakob Yannic Maus TU Graz Graz Austria

ISBN: (纸本)9798400718854

The Δ-vertex coloring problem has become one of the prototypical problems for understanding the complexity of local distributed graph problems on constant-degree graphs. The major open problem is whether the problem can be solved deterministically in logarithmic time, which would match the lower bound [Chang et al., FOCS'16]. Despite recent progress in the design of efficient Δ-coloring algorithms, there is currently a polynomial gap between the upper and lower *** this work we present a O (log n)-round deterministic Δ-coloring algorithm for dense constant-degree graphs, matching the lower bound for the problem on general graphs. For general Δ the algorithms' complexity is min{Õ(log5/3 n), O (Δ + log n)}. All recent distributed and sublinear graph coloring algorithms (also for coloring with more than Δ colors) decompose the graph into sparse and dense parts. Our algorithm works for the case that this decomposition has no sparse vertices. Ironically, in recent (randomized) Δ-coloring algorithms, dealing with sparse parts was relatively easy and these dense parts arguably posed the major hurdle. We present a solution that addresses the dense parts and may have the potential for extension to sparse *** approach is fundamentally different from prior deterministic algorithms and hence hopefully contributes towards designing an optimal algorithm for the general case. Additionally, we leverage our result to also obtain a randomized min{Õ(log5/3 log n), O (Δ + log log n)}-round algorithm for Δ-coloring dense graphs that also matches the lower bound for the problem on general constant-degree graphs [Brandt et al.; STOC'16].

关键词： distributed algorithms

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Brief Announcement: The Complexity Landscape of Dynamic distributed Subgraph Finding 25

Brief Announcement: The Complexity Landscape of Dynamic Dist...

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Proceedings of the ACM Symposium on Principles of distributed Computing

作者： Yi-Jun Chang Lyuting Chen Yanyu Chen Gopinath Mishra Mingyang Yang National University of Singapore Singapore Singapore

ISBN: (纸本)9798400718854

Bonne and Censor-Hillel (ICALP 2019) initiated the study of distributed subgraph finding in dynamic networks of limited bandwidth. For the case where the target subgraph is a clique, they determined the tight bandwidth complexity bounds in nearly all settings. However, several open questions remain, and very little is known about finding subgraphs beyond cliques. In this work, we consider these questions and explore subgraphs beyond *** finding cliques, we establish an Ω (log log n) bandwidth lower bound for one-round membership-detection under edge insertions only and an Ω (log log log n) bandwidth lower bound for one-round detection under both edge insertions and node insertions. Moreover, we demonstrate new algorithms to show that our lower bounds are tight in bounded-degree networks when the target subgraph is a triangle. Prior to our work, no lower bounds were known for these *** finding subgraphs beyond cliques, we present a complete characterization of the bandwidth complexity of the membership-listing problem for every target subgraph, every number of rounds, and every type of topological change: node insertions, node deletions, edge insertions, and edge deletions. We also show partial characterizations for one-round membership-detection and listing.

关键词： distributed algorithms

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distributed Policy Evaluation with Local Updates over Time-Varying Communication Network

Distributed Policy Evaluation with Local Updates over Time-V...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Matthew Crespo Chinwendu Enyioha Electrical and Computer Engineering University of Central Florida Orlando United States of America

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

This paper studies a consensus-based policy evaluation algorithm in a cooperative team of heterogeneous learners. To improve each agent’s approximation of their value function, they each update their weight parameters, then perform consensus updates with neighboring agents over a dynamic communication network. We present the analysis of an efficient fully distributed algorithm for cooperatively evaluating and optimizing their value functions over a time-varying communication network in the average-reward setting. Our main result shows that, using this algorithm, the agents can approximate the true value function, with the approximation error dependent on the number of communication rounds and number of time steps it takes for the communication network to be connected. To validate the theoretical results, we present accompanying numerical experiments that show the theoretical error bounds are not only tight, but get tighter as the time steps required to guarantee network connectivity reduces.

关键词： Heuristic algorithms Approximation algorithms Approximation error Communication networks distributed algorithms Time-varying systems

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distributed Generalized Nash Equilibria Computation of Monotone Games via Double-Layer Preconditioned Proximal-Point algorithms

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2019年第1期6卷 299-311页

作者： Yi, Peng Pavel, Lacra Washington Univ Dept Elect & Syst Engn St Louis MO 63130 USA Univ Toronto Dept Elect & Comp Engn Toronto ON M5S Canada

In this paper, we investigate distributed generalized Nash equilibrium (GNE) computation of monotone games with affine coupling constraints. Each player can only utilize its local objective function, local feasible set, and a local block of the coupling constraint, and can only communicate with its neighbors. We assume the game has monotone pseudo-subdifferential without Lipschitz continuity restrictions. We design novel center-free distributed GNE seeking algorithms for equality and inequality affine coupling constraints, respectively. A proximal alternating direction method of multipliers is proposed for the equality case, while for the inequality case, a parallel splitting type algorithm is proposed. In both algorithms, the GNE seeking task is decomposed into a sequential Nash equilibrium (NE) computation of regularized subgames and distributed update of multipliers and auxiliary variables, based on local data and local communication. Our two double-layer GNE algorithms need not specify the inner loop NE seeking algorithm, and moreover, only require that the strongly monotone subgames are inexactly solved. We prove their convergence by showing that the two algorithms can be seen as specific instances of preconditioned proximal point algorithms for finding zeros of monotone operators. Applications and numerical simulations are given for illustration.

关键词： distributed algorithms game theory networks

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Performance of distributed OPF Algorithm with ZIP Loads and Different PV Inverter Modes

Performance of Distributed OPF Algorithm with ZIP Loads and ...

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International Conference on Power Electronic Drives

作者： Subho Paul Department of Electrical Engineering Indian Institute of Technology (BHU) Varanasi Varanasi Uttar Pradesh India

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

High penetration of inverter-associated renewable energy resources, especially solar photovoltaic (PV) panels, increases the number of controllable grid-edge devices in an electric grid. Optimal power flow (OPF) problems are often formulated to ensure such electric grids' economic, secure, and reliable operation. To overcome the scalability and computation challenges related to centralized OPF methods, distributed algorithms are adopted for medium and bulk distribution grids. However, the convergence of distributed algorithms is highly impacted by the network architecture and operating characteristics of the grid edge resources. Studies are abundant on the variation in network architecture, but no study has investigated the impact of load models and PV inverter operating modes on the performance of distributed OPF algorithms. To address this, in this article loads are modeled as both constant power and voltage-dependent ZIP loads, and PV inverters are modeled considering their four distinct operating modes viz. grid following (GFM), volt-var controlling (VVCM), volt-watt controlling (VWCM), and voltage control (VCM). The load and the PV inverter models are integrated with the consensus Alternating direction method of multiplier (ADMM)-based distributed OPF algorithm as constraints for minimizing total network loss for an unbalanced distribution network. The performance characterization is performed on the IEEE 123 bus test system concerning network loss values, solution time, and total number of required macro iterations.

关键词： Biological system modeling Simulation Network architecture Inverters Convex functions Voltage control distributed algorithms Load modeling Convergence Load flow

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An Accelerated Algorithm for distributed Sparse PCA Problems with $l_{2,1}\text{-norm}$ Regularization

An Accelerated Algorithm for Distributed Sparse PCA Problems...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Kaiwei Yang Runyao Liu Junpeng Zhou Qia Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Sparse Principal Component Analysis (Sparse PCA) is a widely adopted technique for feature extraction in high-dimensional datasets. Based on Alternating Direction Method of Multipliers (ADMM) framework, a Privacy-Aware distributed Subspace Splitting Algorithm (DSSAL1) is recently proposed in [17] for the sparse PCA with $\mathbf{l}_{\mathbfdistributed}-\mathbf{norm}$ regularization. In particular, the algorithm adapting a subspace-splitting strategy achieves low communication and fast convergence. In view of the group-sparsity of the latent feature of the datum, we generalize distributed Subspace Splitting Algorithm to handle Sparse PCA model with $\mathbf{l}_{\mathbf{21}}-\mathbf{norm}$ regularization. Our principal technical innovation lies in integrating the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) [18] as an inertial acceleration method for solving the central node's optimization subproblem. Comprehensive experiments on synthetic datasets and real-world datasets consistently validate the algorithm's computational efficiency.

关键词： Privacy Technological innovation Computational modeling Iterative algorithms Computational efficiency distributed algorithms Optimization Principal component analysis Convergence Synthetic data

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How to Optimally Reconfigure Average Consensus with Maximum-Degree Weights in Bipartite Regular Graphs 1

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6th Computational Methods in Systems and Software, CoMeSySo 2022

作者： Kenyeres, Martin Kenyeres, Jozef Institute of Informatics Slovak Academy of Sciences Dubravska cesta 9 Bratislava84507 Slovakia Frequentis AG Innovationsstraße 1 Vienna1100 Austria

ISBN: (数字)9783031214356

ISBN: (纸本)9783031214349

The average consensus algorithm is a frequently applied iterative scheme to efficiently estimate various aggregate functions in many modern multi-agent systems. This paper addresses the average consensus with the Maximum-degree weights in bipartite regular graphs, where this algorithm does not converge as the third convergence condition is broken in these graphs. We apply a distributed mechanism (determined by five conditions) for detecting whether or not the algorithm is executed in this critical graph topology and analyze how to reconfigure it the most efficiently. The latest research suggests that its high performance is likely not to be achieved with the same values of the mixing parameter as in non-bipartite non-regular topologies. Hence, our goal is to identify the reconfiguration of the weight matrix ensuring the optimal performance of the algorithm under various conditions. Its performance is examined in randomly generated bipartite regular graphs with various connectivity and under three scenarios differing from each other in the rounding accuracy. Also, the asymptotic convergence factor of the weight matrices after reconfiguration is examined in the experimental section. Besides, the performance of a reconfigured average consensus with the Maximum-degree weights in bipartite regular graphs is compared to the Metropolis-Hastings algorithm, which also diverges in these topologies with its optimal initial configuration. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Average consensus algorithm Bipartite regular graphs Consensus Data aggregation distributed algorithms Maximum-degree weights Multi-agent systems

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