Efficient randomized algorithms for PageRank problem

Abstract

In the paper we compare well known numerical methods of finding PageRank vector. We propose Markov Chain Monte Carlo method and obtain a new estimation for this method. We also propose a new method for PageRank problem based on the reduction of this problem to the matrix game. We solve this (sparse) matrix game with randomized mirror descent. It should be mentioned that we used non-standard randomization (in KL-projection) goes back to Grigoriadis-Khachiayn (1995).