论文标题
高阶马尔可夫链的加速功率方法
Accelerating Power Methods for Higher-order Markov Chains
论文作者
论文摘要
高阶马尔可夫连锁店在许多领域都起着非常重要的作用，从多线性pagerank到财务建模。在本文中，我们提出了三种加速的高阶功率方法，用于计算高阶马尔可夫链的限制概率分布，即具有动量和高阶二次外推法的高阶功率方法。建立了收敛结果，并报告了数值实验以显示所提出的算法的效率。特别是，非参数二次外推法非常有竞争力，并且表现优于最先进的比赛。
Higher-order Markov chains play a very important role in many fields, ranging from multilinear PageRank to financial modeling. In this paper, we propose three 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 the efficiency of the proposed algorithms. In particular, the non-parametric quadratic extrapolation method is very competitive, and outperforms state-of-the-art competitions.
