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Asynchronous Gossip for Averaging and Spectral Ranking

DSpace at IIT Bombay

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Title Asynchronous Gossip for Averaging and Spectral Ranking
 
Creator BORKAR, VS
MAKHIJANI, R
SUNDARESAN, R
 
Subject Gossip algorithm
asynchronous stochastic approximation
Poisson equation
learning
Perron-Frobenius eigenvector
ranking
MARKOV DECISION-PROCESSES
RISK-SENSITIVE CONTROL
STOCHASTIC APPROXIMATIONS
ALGORITHMS
CONSENSUS
EIGENVECTOR
NETWORKS
PAGERANK
MATRIX
CHAIN
 
Description We consider two variants of the classical gossip algorithm. The first variant is a version of asynchronous stochastic approximation. We highlight a fundamental difficulty associated with the classical asynchronous gossip scheme, viz., that it may not converge to a desired average, and suggest an alternative scheme based on reinforcement learning that has guaranteed convergence to the desired average. We then discuss a potential application to a wireless network setting with simultaneous link activation constraints. The second variant is a gossip algorithm for distributed computation of the Perron-Frobenius eigenvector of a nonnegative matrix. While the first variant draws upon a reinforcement learning algorithm for an average cost controlled Markov decision problem, the second variant draws upon a reinforcement learning algorithm for risk-sensitive control. We then discuss potential applications of the second variant to ranking schemes, reputation networks, and principal component analysis.
 
Publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
 
Date 2014-12-29T04:31:55Z
2014-12-29T04:31:55Z
2014
 
Type Article
 
Identifier IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 8(4)703-716
1932-4553
1941-0484
http://dx.doi.org/10.1109/JSTSP.2014.2320229
http://dspace.library.iitb.ac.in/jspui/handle/100/17083
 
Language English