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

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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VI-constrained hemivariational inequalities: distributed algorithms and power control in ad-hoc networks

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MATHEMATICAL PROGRAMMING 2014年第1-2期145卷 59-96页

作者： Facchinei, Francisco Pang, Jong-Shi Scutari, Gesualdo Lampariello, Lorenzo Univ Roma La Sapienza Dept Comp Control & Management Engn Antonio Ruber I-00185 Rome Italy Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL USA SUNY Buffalo Dept Elect Engn Buffalo NY 14260 USA

We consider centralized and distributed algorithms for the numerical solution of a hemivariational inequality (HVI) where the feasible set is given by the intersection of a closed convex set with the solution set of a lower-level monotone variational inequality (VI). The algorithms consist of a main loop wherein a sequence of one-level, strongly monotone HVIs are solved that involve the penalization of the non-VI constraint and a combination of proximal and Tikhonov regularization to handle the lower-level VI constraints. Minimization problems, possibly with nonconvex objective functions, over implicitly defined VI constraints are discussed in detail. The methods developed in the paper are then used to successfully solve a new power control problem in ad-hoc networks.

关键词： Hemivariational inequality Hierarchical optimization distributed algorithms Penalization Power control Ad-hoc networks

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Accommodating Strategic Players in distributed algorithms for Power Dispatch Problems

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第11期52卷 12594-12603页

作者： Chen, Sijie Xu, Chengke Yan, Zheng Guan, Xinping Le, Xinyi Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Minist Educ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Key Lab Syst Control & Informat Proc Dept AutomatMinist Educ China Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Shanghai Engn Res Ctr Intelligent Control & Manag Shanghai 200240 Peoples R China

distributed algorithms are gaining increasing research interests in the area of power system optimization and dispatch. Existing distributed power dispatch algorithms (DPDAs) usually assume that suppliers/consumers bid truthfully. However, this article shows the need for DPDAs to consider strategic players and to take account of their behavior deviation from what the DPDAs expect. To address this, we propose a distributed strategy update algorithm (DSUA) on top of a DPDA. The DSUA considers strategic suppliers who optimize their bids in a DPDA, using only the information accessible from a DPDA, that is, price. The DSUA also considers the cases when suppliers update bids alternately or simultaneously. Under both cases, we show the closeness of supplier bids to the Nash equilibrium via game-theoretic analysis as well as simulation.

关键词： Games Nash equilibrium distributed algorithms Cost function Convergence Analytical models Time-domain analysis distributed algorithm game theory Nash equilibrium neurodynamic optimization strategic bidding

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Modeling and Proving distributed algorithms for Dynamic Graphs

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 108卷 751-761页

作者： Fakhfakh, Faten Tounsi, Mohamed Mosbah, Mohamed Univ Sfax ReDCAD Lab Sfax Tunisia Univ Bordeaux LaBRI Bordeaux INP CNRSUMR 5800 F-33400 Talence France

Proving the correctness of distributed algorithms is a difficult challenge in dynamic networks. In these networks, topological changes can take place due to unexpected appearance and disappearance of devices and communication links. The existing studies in the literature lack a general model for their development and their proof. Besides, most of the proofs which have been introduced are handwritten. Our work aims at providing a reusable approach which reduces the specification and verification efforts of distributed algorithms. At the first stage, we present a basic pattern which aims to deal with topological events. At the second stage, we extend this pattern to construct and maintain a spanning forest in a dynamic network. We illustrate the extended pattern through the leader election algorithm. We show the efficiency of our approach by presenting the number of proofs associated with the development of the algorithm with and without using the pattern. (C) 2020 Elsevier B.V. All rights reserved.

关键词： distributed algorithms Dynamic networks Refinement Formal pattern Evolving graphs

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THE BENEVOLENT BANDIT LABORATORY - A TESTBED FOR distributed algorithms

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 1989年第2期7卷 303-311页

作者： FELDERMAN, RE SCHOOLER, EM KLEINROCK, L INFORMAT SCI INST DIV SYSTMARINA DEL REYCA 90292

The design, implementation, and use of a distributed processing environment on a network of IBM PCs running DOS is described. Temporarily unused PCs can be accessed by other users on the network to perform distributed computations. An owner of a PC need not be aware that the machine is being used during idle times; the machine is immediately returned when the owner begins to work again. Some degree of computation resiliency is provided in this unreliable environment; if a PC is part of a distributed algorithm and is reclaimed by its owner, the system finds a replacement node (if possible), resends the affected code to the processor, and restarts it. Thus, a distributed computation is able to proceed despite a set of transient processors. System performance, distributed applications, and fault tolerance are discussed. Performance improvements are demonstrated by applications like parallel merge sort and a distributed search solution to the eight puzzle.< >

关键词： Laboratories Testing distributed algorithms Microcomputers Time sharing computer systems Computer networks distributed computing Local area networks Computer science System performance

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