Fast Distributed Algorithms for Computing Separable Functions
Damon Mosk-Aoyama,Devavrat Shah +1 more
TLDR
The main contribution of this paper is the design of a distributed randomized algorithm for computing separable functions that is shown to depend on the running time of a minimum computation algorithm used as a subroutine.Abstract:
The problem of computing functions of values at the nodes in a network in a fully distributed manner, where nodes do not have unique identities and make decisions based only on local information, has applications in sensor, peer-to-peer, and ad hoc networks. The task of computing separable functions, which can be written as linear combinations of functions of individual variables, is studied in this context. Known iterative algorithms for averaging can be used to compute the normalized values of such functions, but these algorithms do not extend, in general, to the computation of the actual values of separable functions. The main contribution of this paper is the design of a distributed randomized algorithm for computing separable functions. The running time of the algorithm is shown to depend on the running time of a minimum computation algorithm used as a subroutine. Using a randomized gossip mechanism for minimum computation as the subroutine yields a complete fully distributed algorithm for computing separable functions. For a class of graphs with small spectral gap, such as grid graphs, the time used by the algorithm to compute averages is of a smaller order than the time required by a known iterative averaging scheme.read more
Citations
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Journal ArticleDOI
Gossip Algorithms for Distributed Signal Processing
TL;DR: An overview of recent gossip algorithms work, including convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping, and the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.
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Metaheuristics in large-scale global continues optimization
TL;DR: The paper mainly covers the fundamental algorithmic frameworks such as decomposition and non-decomposition methods, and their current applications in the field of large-scale global optimization.
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Gossip Algorithms
TL;DR: A systematic survey of many of the recent results on Gossip network algorithms, which utilize interdisciplinary tools from Markov chain theory, Optimization, Percolation, Random graphs, Spectral graph theory, and Coding.
Journal ArticleDOI
Distributed particle filtering in agent networks: A survey, classification, and comparison
TL;DR: A survey, classification, and comparison of various DPF approaches and algorithms available to date are presented, with emphasis on decentralized ANs that do not include a central processing or control unit.
Journal ArticleDOI
Likelihood Consensus and Its Application to Distributed Particle Filtering
TL;DR: In this paper, a distributed method for computing, at each sensor, an approximation of the joint likelihood function (JLF) by means of consensus algorithms is proposed, which is applicable if the local likelihood functions of the various sensors (viewed as conditional probability density functions of local measurements) belong to the exponential family of distributions.
References
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Book
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TL;DR: This work discusses parallel and distributed architectures, complexity measures, and communication and synchronization issues, and it presents both Jacobi and Gauss-Seidel iterations, which serve as algorithms of reference for many of the computational approaches addressed later.
Book
Large Deviations Techniques and Applications
Amir Dembo,Ofer Zeitouni +1 more
TL;DR: The LDP for Abstract Empirical Measures and applications-The Finite Dimensional Case and Applications of Empirically Measures LDP are presented.
Proceedings ArticleDOI
Epidemic algorithms for replicated database maintenance
Alan J. Demers,Daniel H. Greene,Carl Hauser,Wes Irish,John Larson,Scott Shenker,Howard E. Sturgis,Dan Swinehart,Douglas B. Terry +8 more
TL;DR: This paper descrikrs several randomized algorit, hms for dist,rihut.ing updates and driving t,he replicas toward consist,c>nc,y.
Journal ArticleDOI
Distributed asynchronous deterministic and stochastic gradient optimization algorithms
TL;DR: A model for asynchronous distributed computation is presented and it is shown that natural asynchronous distributed versions of a large class of deterministic and stochastic gradient-like algorithms retain the desirable convergence properties of their centralized counterparts.
Proceedings ArticleDOI
Gossip-based computation of aggregate information
TL;DR: This paper analyzes the diffusion speed of uniform gossip in the presence of node and link failures, as well as for flooding-based mechanisms, and shows that this diffusion speed is at the heart of the approximation guarantees for all of the above problems.