A randomized algorithm for finding eigenvector of stochastic matrix with application to PageRank problem | IEEE Conference Publication | IEEE Xplore

A randomized algorithm for finding eigenvector of stochastic matrix with application to PageRank problem


Abstract:

The problem of finding the eigenvector corresponding to the largest eigenvalue of a stochastic matrix has numerous applications in ranking search results, multi-agent con...Show More

Abstract:

The problem of finding the eigenvector corresponding to the largest eigenvalue of a stochastic matrix has numerous applications in ranking search results, multi-agent consensus, networked control and data mining. The well known power method is a typical tool for its solution. However randomized methods could be competitors vs standard ones; they require much less calculations for one iteration and are well tailored for distributed computations. We propose a new randomized algorithm and provide an explicit upper bound for its rate of convergence O(radicInN/n) where N is the dimension and n is the number of iterations. The bound looks promising because radicInN is not large even for very high dimensions. The algorithm is based on the mirror-descent method for convex stochastic optimization.
Date of Conference: 08-10 July 2009
Date Added to IEEE Xplore: 09 October 2009
ISBN Information:
Print ISSN: 1085-1992
Conference Location: St. Petersburg, Russia

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