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IEEE Conference on Decision and Control

作者： Hongsheng Qi Bo Li Rui-Juan Jing Alexandre Proutiere Guodong Shi Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of Mathematics Mechanization Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Faculty of Science Jiangsu University Zhenjiang China KTH Royal Institute of Technology Stockholm Sweden Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW Australia

ISBN: (数字)9781728174471

ISBN: (纸本)9781728174488

In this paper, we propose distributed algorithms that solve a system of Boolean equations over a network, where each node in the network possesses only one Boolean equation from the system. The Boolean equation assigned at any particular node is a private equation known to this node only, and the nodes aim to compute the exact set of solutions to the system without exchanging their local equations. We show that each private Boolean equation can be locally lifted to a linear algebraic equation under a basis of Boolean vectors, leading to a network linear equation that is distributedly solvable. A number of exact or approximate solutions to the induced linear equation are then computed at each node from different initial values. The solutions to the original Boolean equations are eventually computed locally via a Boolean vector search algorithm. We prove that given solvable Boolean equations, when the initial values of the nodes for the distributed linear equation solving step are i.i.d selected according to a uniform distribution in a high-dimensional cube, our algorithms return the exact solution set of the Boolean equations at each node with high probability.

关键词： Signal processing algorithms Heuristic algorithms Consensus algorithm distributed algorithms Approximation algorithms Signal processing Convergence

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distributed Integer Weight Balancing in the Presence of Time Delays in Directed Graphs

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2018年第3期5卷 1300-1309页

作者： Rikos, Apostolos I. Hadjicostis, Christoforos N. Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus Univ Illinois Dept Elect & Comp Engn Champaign IL 61801 USA

Adigraph with positive weights on its edges is weight-balanced if, for each node, the sum of the weights of the incoming edges is equal to the sum of the weights of the outgoing edges. Weight-balanced digraphs play an important role in a variety of cooperative control problems, including formation control, event-triggered coordination, distributed averaging, and optimization. Classical distributed algorithms for asymptotic average consensus typically assume timely and reliable exchange of information between neighboring components of a given multicomponent system. These assumptions are not necessarily valid in practice due to varying delays that might affect computations at different nodes and/or transmissions at different links. In this paper, we propose a distributed algorithm that solves the integer weight balancing problem in the presence of arbitrary (time-varying and inhomogeneous) time delays that might affect the transmission at a particular link at a particular time. The algorithm converges after a finite number of steps (that we explicitly bound) in the presence of bounded delays and converges in finite time (with probability one) in the case of unbounded delays (packet drops). Furthermore, we show that the resulting weight-balanced digraph is unique regardless of how time delays or packet drops manifest themselves. We also analyze the computational and communication complexity of the proposed algorithm, and provide examples to illustrate its operation and performance.

关键词： Digraphs distributed algorithms finite time convergence packet drops transmission delays weight balancing

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distributed Nonasymptotic Confidence Region Computation Over Sensor Networks

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2018年第2期4卷 308-324页

作者： Zambianchi, Vincenzo Bassi, Francesca Calisti, Alex Dardari, Davide Kieffer, Michel Pasolini, Gianni Univ Bologna Wireless Commun Lab Wilab Dept Elect Elect & Informat Engn G Marconi DEI I-40126 Bologna Italy ESME Sudria F-94200 Ivry France L2S F-91192 Gif Sur Yvette France Univ Paris Sud CNRS Cent Supelec L2S F-91192 Gif Sur Yvette France

This paper addresses the distributed computation of exact, nonasymptotic confidence regions for the parameter estimation of a linear model from observations at different nodes of a network of sensors. If a central unit gathers all the data, the sign perturbed sums (SPS) method proposed by Csaji et al. can be used to define guaranteed confidence regions with prescribed confidence levels from a finite number of measurements. SPS requires only mild assumptions on the measurement noise. This work proposes distributed solutions, based on SPS and suited to a wide variety of sensor networks, for distributed in-node evaluation of nonasymptotic confidence regions as defined by SPS. More specifically, a tagged and aggregated sum information diffusion algorithm is introduced, which exploits the specificities of SPS to avoid flooding the network with all measurements provided by the sensors. The performance of the proposed solutions is evaluated in terms of required traffic load, both analytically and experimentally on different network topologies. The best information diffusion strategy among nodes depends on how structured the network is.

关键词： Confidence region guaranteed precision distributed algorithms sign-perturbed sums

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Social Learning and distributed Hypothesis Testing

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IEEE TRANSACTIONS ON INFORMATION THEORY 2018年第9期64卷 6161-6179页

作者： Lalitha, Anusha Javidi, Tara Sarwate, Anand D. Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA Rutgers State Univ Dept Elect & Comp Engn Piscataway NJ 08854 USA

This paper considers a problem of distributed hypothesis testing over a network. Individual nodes in a network receive noisy local (private) observations whose distribution is parameterized by a discrete parameter (hypothesis). The marginals of the joint observation distribution conditioned on each hypothesis are known locally at the nodes, but the true parameter/hypothesis is not known. An update rule is analyzed in which nodes first perform a Bayesian update of their belief (distribution estimate) of each hypothesis based on their local observations, communicate these updates to their neighbors, and then perform a "non-Bayesian" linear consensus using the log-beliefs of their neighbors. Under mild assumptions, we show that the belief of any node on a wrong hypothesis converges to zero exponentially fast. We characterize the exponential rate of learning, which we call the network divergence, in terms of the nodes' influence of the network and the divergences between the observations' distributions. For a broad class of observation statistics which includes distributions with unbounded support such as Gaussian mixtures, we show that rate of rejection of wrong hypothesis satisfies a large deviation principle, i.e., the probability of sample paths on which the rate of rejection of wrong hypothesis deviates from the mean rate vanishes exponentially fast and we characterize the rate function in terms of the nodes' influence of the network and the local observation models.

关键词： distributed algorithms message passing rate of learning large deviation principle

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Communication-efficient distributed Solutions to a System of Linear Equations with Laplacian Sparse Structure 57

Communication-efficient Distributed Solutions to a System of...

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57th IEEE Conference on Decision and Control (CDC)

作者： Wang, Peng Gao, Yuanqi Yu, Nanpeng Ren, Wei Lian, Jianming Wu, Di Pacific Northwest Natl Lab 902 Battelle Blvd Richland WA 99352 USA Univ Calif Riverside Dept Elect & Comp Engn 900 Univ Ave Riverside CA 92521 USA

ISBN: (纸本)9781538613955

Two communication-efficient distributed algorithms are proposed to solve a system of linear equations Ax = b with Laplacian sparse A. A system of linear equations with Laplacian sparse A can be found in many applications, e.g., the power flow problems and other network flow problems. The first algorithm is based on the gradient descent method in optimization and the agents only share two parts of the system state instead of that of the whole system state, which saves significant communication. The two parts shared by every agent through a communication link are the state information of its own and its neighbor connected by the communication link. The second method is obtained from an approximation to the Newton method, which converges faster. It requires twice as much communication as the first one but is still communication-efficient due to the low dimension of each part shared between agents. The convergence at a linear rate of both methods is proved.

关键词： Laplace equations Sparse matrices distributed algorithms Newton method Convergence Jacobian matrices Mathematical model

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13th ACM Conference on Recommender Systems (RECSYS)

作者： Jeunen, Olivier Verstrepen, Koen Goethals, Bart Univ Antwerp Antwerp Belgium Froomle Antwerp Belgium Monash Univ Clayton Vic Australia

ISBN: (纸本)9781450362436

The problem of computing all pairwise similarities in a large collection of vectors is a well-known and common data mining task. As the number and dimensionality of these vectors keeps increasing, however, currently existing approaches are often unable to meet the strict efficiency requirements imposed by the environments they need to perform in. Real-time neighbourhood-based collaborative filtering (CF) is one example of such an environment in which performance is critical. In this work, we present a novel algorithm for efficient and exact similarity computation between sparse, high-dimensional vectors. Our approach exploits the sparsity that is inherent to implicit feedback data-streams, entailing significant gains compared to other methods. Furthermore, as our model learns incrementally, it is naturally suited for dynamic real-time CF environments. We propose a MapReduce-inspired parallellisation procedure along with our method, and show how even more speed-up can be achieved. Additionally, in many real-world systems, many items are actually not recommendable at any given time, due to recency, stock, seasonality, or enforced business rules. We exploit this fact to further improve the computational efficiency of our approach. Experimental evaluation on both real-world and publicly available datasets shows that our approach scales up to millions of processed user-item interactions per second, and well advances the state-of-the-art.

关键词： Nearest-neighbours incremental algorithms distributed algorithms

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distributed Garbage Collection for General Graphs 2018

Distributed Garbage Collection for General Graphs

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ACM SIGPLAN International Symposium on Memory Management (ISMM)

作者： Brandt, Steven R. Krishnan, Hari Busch, Costas Sharma, Gokarna Louisiana State Univ Baton Rouge LA 70803 USA Kent State Univ Kent OH 44242 USA

ISBN: (纸本)9781450358019

We propose a scalable, cycle-collecting, decentralized, reference counting garbage collector with partial tracing. The algorithm is based on the Brownbridge system but uses four different types of references to label edges. Memory usage is O(logn) bits per node, where n is the number of nodes in the graph. The algorithm assumes an asynchronous network model with a reliable reordering channel. It collects garbage in O(Ea) time, where Ea is the number of edges in the induced subgraph. The algorithm uses termination detection to manage the distributed computation, a unique identifier to break the symmetry among multiple collectors, and a transaction-based approach when multiple collectors conflict. Unlike existing algorithms, ours is not centralized, does not require barriers, does not require migration of nodes, does not require back-pointers on every edge, and is stable against concurrent mutation.

关键词： distributed algorithms Garbage collection distributed memory distributed Garbage Collection

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Consensus based distributed algorithm for economic dispatch in power systems 14

Consensus based distributed algorithm for economic dispatch ...

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14th IEEE International Conference on Control and Automation (ICCA)

作者： Ma, Kai Yu, Yangqing Zhu, Shanying Yang, Jie Guan, Xinping Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Minist Educ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9781538660898

In this paper, a distributed discrete-time algorithm is presented to solve the economic dispatch problem. We aim to search for the optimal power generations so that the total generation cost is minimized with the supply and demand reaching the balance. The iterative procedure is designed as gradient descent method in combination with a consensus process. Without a central unit, the generators in the power system need to work collaboratively and exchange local information according to the communication topology. Simulation studies are conducted on the IEEE 9 bus system to examine the effectiveness of the proposed algorithm. From the simulation results, we can observe that the proposed algorithm shows the validity and good convergence performance to seek the optimal solution. The balance between supply and demand can be achieved with increasing the economic benefits of the power system.

关键词： Economics Optimization distributed algorithms Generators Heuristic algorithms Power generation Power system stability"

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distributed Online Resource Allocation using Congestion Game for 5G Virtual Network Services

Distributed Online Resource Allocation using Congestion Game...

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IEEE Global Communications Conference

作者： Yu Bi Monchai Bunyakitanon Navdeep Uniyal Anderson Bravalheri Abubakar Muqaddas Reza Nejabati Dimitra Simeonidou Faculty of Engineering High Performance Networks Group University of Bristol United Kingdom

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

To meet the challenge of flexible and dynamic resource provisioning for massive and various network services, we investigate the network function virtualization-resource allocation problem in the 5G network. For the first time, this problem is modelled as the congestion game to capture the effects of resource congestion on packet processing latency, optical-to-electronic and electronic-to-optical conversion latency. All the network service requests received at the same time are players trying to minimise their own end-to-end latency and resource consumption cost. A distributed online algorithm is designed and simulation results show that it can achieve 100% service acceptance ratio while the baseline algorithm cannot. If lower weighted resource consumption cost is set for 1ms and 5ms services, more such services will be routed to edge nodes and network operators will earn more. An experiment for network services with different packet sizes is carried out, and results prove that the proposed algorithm converges to Nash Equilibrium in 40 seconds and the latency requirements are all satisfied if the packet size is small.

关键词： Games Optical fiber networks 5G mobile communication Resource management High-speed optical techniques Heuristic algorithms distributed algorithms

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A distributed Stochastic Gradient Tracking Method 57

A Distributed Stochastic Gradient Tracking Method

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57th IEEE Conference on Decision and Control (CDC)

作者： Pu, Shi Nedic, Angelia Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA

ISBN: (纸本)9781538613955

In this paper, we study the problem of distributed multi-agent optimization over a network, where each agent possesses a local cost function that is smooth and strongly convex. The global objective is to find a common solution that minimizes the average of all cost functions. Assuming agents only have access to unbiased estimates of the gradients of their local cost functions, we consider a distributed stochastic gradient tracking method. We show that, in expectation, the iterates generated by each agent are attracted to a neighborhood of the optimal solution, where they accumulate exponentially fast (under a constant step size choice). More importantly, the limiting (expected) error bounds on the distance of the iterates from the optimal solution decrease with the network size, which is a comparable performance to a centralized stochastic gradient algorithm. Numerical examples further demonstrate the effectiveness of the method.

关键词： Stochastic processes Convergence Cost function Radio frequency Limiting distributed algorithms

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