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

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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MADCAPP - MEASUREMENT AND ANALYSIS OF HIGH-LEVEL COMMUNICATIONS OF ASYNCHRONOUS distributed algorithms ON PARALLEL PROCESSORS

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SIMULATION 1992年第2期59卷 127-133页

作者： GILLIS, CW GHOSH, S [o_1]Division of Engineering Brown University Providence Rhode Island 02912

The issue of correctness of complex asynchronous distributed algorithms implemented on loosely coupled parallel processor systemsis difficult to address given the lack of effective debugging tools. In such systems, messages propagate asynchronously over physical connections and precise knowledge of the state of every message in the system at any instant of time is difficult to obtain. For a particular class of asynchronous distributed algorithms [1,2,5] that may be characterized by independent models that execute asynchronously on the processors and interact with one another only through explicit messages, the following reasoning applies. Information on the flow and content of messages and the activity of the processors is significant towards understanding the functional correctness of the implementation. This paper proposes a new approach, MADCAPP, to measure and analyze high-level message communication and the activity level of the processors.

关键词： DEBUGGING distributed algorithms PARALLEL PROCESSORS VISUAL DISPLAY distributed DEBUGGING

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Approximating the minimum vertex cover in sublinear time and a connection to distributed algorithms

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THEORETICAL COMPUTER SCIENCE 2007年第1-3期381卷 183-196页

作者： Parnas, Michal Ron, Dana Acad Coll Tel Aviv Yaffo Sch Comp Sci Tel Aviv Israel Tel Aviv Univ Dept EE Syst Ramat Aviv Israel

For a given graph G over n vertices, let OPTG denote the size of an optimal solution in G of a particular minimization problem (e.g., the size of a minimum vertex cover). A randomized algorithm will be called an alpha-approximation algorithm with an additive error for this minimization problem if for any given additive error parameter epsilon > 0 it computes a value (OPT) over tilde such that, with probability at least 2/3, it holds that OPTG <= (OPT) over tilde <= alpha .OPTG + epsilon n. Assume that the maximum degree or average degree of G is bounded. In this case, we show a reduction from local distributed approximation algorithms for the vertex cover problem to sublinear approximation algorithms for this problem. This reduction can be modified easily and applied to other optimization problems that have local distributed approximation algorithms, such as the dominating set problem. We also show that for the minimum vertex cover problem, the query complexity of such approximation algorithms must grow at least linearly with the average degree (d) over bar of the graph. This lower bound holds for every multiplicative factor alpha and small constant epsilon as long as (d) over bar = O(n/alpha). In particular this means that for dense graphs it is not possible to design an algorithm whose complexity is o(n). (c) 2007 Elsevier B.V. All rights reserved.

关键词： sublinear approximation algorithms distributed algorithms minimum vertex cover

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Asynchronous distributed algorithms for Solving Linear Algebraic Equations

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2018年第2期63卷 372-385页

作者： Liu, Ji Mou, Shaoshuai Morse, A. Stephen Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Purdue Univ W Lafayette IN 47907 USA Yale Univ New Haven CT 06520 USA

Two asynchronous distributed algorithms are presented for solving a linear equation of the form Ax = b with at least one solution. The equation is simultaneously and asynchronously solved by m agents assuming that each agent knows only a subset of the rows of the partitioned matrix [A b], the estimates of the equation's solution generated by its neighbors, and nothing more. Neighbor relationships are characterized by a time-dependent directed graph whose vertices correspond to agents and whose arcs depict neighbor relationships. Each agent recursively updates its estimate of a solution at its own event times by utilizing estimates generated by its neighbors which are transmitted with delays. The event time sequences of different agents are not assumed to be synchronized. It is shown that for any matrix-vector pair (A, b) for which the equation has a solution and any repeatedly jointly strongly connected sequence of neighbor graphs defined on the merged sequence of all agents' event times, the algorithms cause all agents' estimates to converge exponentially fast to the same solution to Ax = b. The first algorithm requires a specific initialization step at each agent, and the second algorithm works for arbitrary initializations. Explicit expressions for convergence rates are provided, and a relation between local initializations and limiting consensus solutions is established, which is used to solve the least 2-norm solution.

关键词： Asynchronous systems autonomous agents distributed algorithms multi-agent systems

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Recovery of Time-Varying Graph Signals via distributed algorithms on Regularized Problems

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2020年 6卷 540-555页

作者： Jiang, Junzheng Tay, David B. Sun, Qiyu Ouyang, Shan Guilin Univ Elect Technol Sch Informat & Commun Guilin 541004 Peoples R China Deakin Univ Sch Informat Technol Waurn Ponds Vic 3216 Australia Univ Cent Florida Dept Math Orlando FL 32816 USA

The recovery of missing samples from available noisy measurements is a fundamental problem in signal processing. This process is also sometimes known as graph signal inpainting, reconstruction, forecasting or inference. Many of the existing algorithms do not scale well with the size of the graph and/or they cannot be implemented efficiently in a distributed manner. In this paper, we develop efficient distributed algorithms for the recovery of time-varying graph signals. The a priori assumptions are that the signal is smooth with respect to the graph topology and correlative across time. These assumptions can be incorporated in an optimization formulation of the algorithm via Tikhonov regularization terms. Our formulation is tailored to yield algorithms that can be efficiently implemented in a distributed manner. Two different distributed algorithms, arising from two different formulations, are proposed to solve the optimization problems. The first involves the l(2)-norm, and a distributed least squared recovery algorithm (DLSRA) is proposed that leverages the graph topology and sparsity of the corresponding Hessian matrix. Updates of the Hessian inverse are not required here. The second involves the l(1)-norm and the philosophy of the alternating direction method of multipliers (ADMM) is utilized to develop the algorithm. An inexact Newton method is incorporated into the conventional ADMM to give a distributed ADMM recovery algorithm (DAMRA). The two distributed algorithms require only data exchanges between vertices in localized neighbourhood subgraphs. Experiments on a variety of synthetic and real-world datasets demonstrate that the proposed algorithms are superior to the existing methods in terms of the computational complexity and convergence rate.

关键词： distributed algorithms Signal processing algorithms Topology Optimization Signal processing Convex functions Convergence Graph signal recovery distributed algorithm least squares method alternative direction method of multipliers (ADMM)

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User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of distributed algorithms

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-12页

作者： Astigarraga, Aitzol Arruti, Andoni Muguerza, Javier Santana, Roberto Martin, Jose I. Sierra, Basilio Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Fac Comp Sci Donostia San Sebastian 20018 Spain Univ Basque Country UPV EHU Dept Comp Architecture & Technol Fac Comp Sci Donostia San Sebastian 20018 Spain

Brain-Computer Interfaces (BCIs) have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram-(EEG-) based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range;and, then, an Estimation of Distribution Algorithm(EDA) is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.

关键词： MOTOR ability BRAIN-computer interfaces ELECTROENCEPHALOGRAPHY ESTIMATION theory distributed algorithms

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Designing linear distributed algorithms with memory for fast convergence

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2012年第15期22卷 1691-1702页

作者： Roy, Sandip Wan, Yan Saberi, Ali Xue, Mengran Washington State Univ Dept Elect Engn Pullman WA 99164 USA Univ N Texas Dept Elect Engn Denton TX 76203 USA

Motivated by both distributed computation and decentralized control applications, we studied the distributed linear iterative algorithms with memory. Specifically, we showed that the system of linear equations Gx = b b can be solved through a distributed linear iteration for arbitrary invertible G using only a single memory element at each processor. Further, we demonstrated that the memoried distributed algorithm can be designed to achieve much faster convergence than a memoryless distributed algorithm. Two small simulation examples were included to illustrate the results. Copyright (c) 2011 John Wiley & Sons, Ltd.

关键词： distributed algorithms large-scale networks decentralized control theory numerical algorithms

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FULLY distributed algorithms FOR CONVEX OPTIMIZATION PROBLEMS

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SIAM JOURNAL ON OPTIMIZATION 2010年第6期20卷 3260-3279页

作者： Mosk-Aoyama, Damon Roughgarden, Tim Shah, Devavrat Stanford Univ Dept Comp Sci Stanford CA 94305 USA MIT Dept Elect Engn & Comp Sci Lab Informat & Decis Syst Cambridge MA 02139 USA

We design and analyze a fully distributed algorithm for convex constrained optimization in networks without any consistent naming infrastructure. The algorithm produces an approximately feasible and near-optimal solution in time polynomial in the network size, the inverse of the permitted error, and a measure of curvature variation in the dual optimization problem. It blends, in a novel way, gossip-based information spreading, iterative gradient ascent, and the barrier method from the design of interior-point algorithms.

关键词： convex optimization distributed algorithms gradient ascent

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Differentially Private distributed algorithms for Aggregative Games With Guaranteed Convergence

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第8期69卷 5168-5183页

作者： Wang, Yongqiang Nedic, Angelia Clemson Univ Dept Elect & Comp Engn Clemson SC 29634 USA Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85281 USA

The distributed computation of a Nash equilibrium in aggregative games is gaining increased attention in recent years. Of particular interest is the coordinator-free scenario where individual players only observe the decisions of their neighbors due to practical constraints. Given the noncooperative relationship among participating players, protecting the privacy of individual players becomes imperative when sensitive information is involved. We propose a fully distributed equilibrium-seeking approach for aggregative games that can achieve both rigorous differential privacy and guaranteed computation accuracy of the Nash equilibrium. This is in sharp contrast to existing differential-privacy solutions for aggregative games that have to either sacrifice the accuracy of equilibrium computation to gain rigorous privacy guarantees or allow the cumulative privacy budget to grow unbounded, hence, losing privacy guarantees as iteration proceeds. Our approach uses independent noises across players, thus making it effective even when adversaries have access to all shared messages as well as the underlying algorithm structure. The encryption-free nature of the proposed approach also ensures efficiency in computation and communication. The approach is also applicable in stochastic aggregative games, able to ensure both rigorous differential privacy and guaranteed computation accuracy of the Nash equilibrium when individual players only have stochastic estimates of their pseudogradient mappings. Numerical comparisons with existing counterparts confirm the effectiveness of the proposed approach.

关键词： Games Privacy Convergence Nash equilibrium Differential privacy Aggregates distributed algorithms Aggregative games distributed Nash equilibrium seeking differential privacy

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Secured distributed algorithms without hardness assumptions

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JOURNAL OF PARALLEL AND distributed COMPUTING 2023年 171卷 130-140页

作者： Barenboim, Leonid Levin, Harel Open Univ Israel Dept Math & Comp Sci POB 808 Raanana Israel Nucl Res Ctr Negev Dept Phys POB 9001 Beer Sheva Israel

We study algorithms in the LOCAL model that produce secured output. Specifically, each vertex computes its part in the output, the entire output is correct, but each vertex cannot discover the output of other vertices, with a certain probability. As the extensive research in the distributed algorithms field yielded efficient decentralized algorithms, the discussion about the security of distributed algorithms was somewhat neglected. Nevertheless, many protocols and algorithms were devised in the research area of secure multi-party computation problem. However, the focus in those protocols was to work for every function f at the expense of increasing the round complexity, or the necessity of several computational assumptions. We present a novel approach, which identifies and develops those algorithms that are inherently secure (which means they do not require any further constructions). This approach yields efficient secure algorithms for various labeling and decomposition problems without requiring any hardness assumption, but only a private randomness generator in each vertex.(c) 2022 Elsevier Inc. All rights reserved.

关键词： distributed algorithms Privacy preserving Graph coloring Generic algorithms Multi-party computation

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