Accelerating Power Methods for Higher-order Markov Chains release_vdnjogqtk5hspizxs72wv75foq

by Gaohang Yu, Yi Zhou, Laishui Lv

Released as a article .

2020  

Abstract

Higher-order Markov chains play a very important role in many fields, ranging from multilinear PageRank to financial modeling. In this paper, we propose two accelerated higher-order power methods for computing the limiting probability distribution of higher-order Markov chains, namely higher-order power method with momentum and higher-order quadratic extrapolation method. The convergence results are established, and numerical experiments are reported to show that the proposed algorithms are effective. In particular, the non-parametric higher-order quadratic extrapolation method is very competitive to some existing methods in the literature.
In text/plain format

Archived Files and Locations

application/pdf  184.3 kB
file_5xh4y7gmg5axdisnpv3rgdrudi
web.archive.org (webarchive)
arxiv.org (repository)
Read Archived PDF
Archived
Type  article
Stage   submitted
Date   2020-07-11
Version   v2
Language   en ?
arXiv  2003.00686v2
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 3b930b86-efac-42e7-8278-ec63ac1c207c
API URL: JSON