文章基本信息

标题：Choice and Bias in Random Walks
本地全文：下载
作者：Agelos Georgakopoulos ; John Haslegrave ; Thomas Sauerwald 等
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2020
卷号：151
页码：1-19
DOI：10.4230/LIPIcs.ITCS.2020.76
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：We analyse the following random walk process inspired by the power-of-two-choice paradigm: starting from a given vertex, at each step, unlike the simple random walk (SRW) that always moves to a randomly chosen neighbour, we have the choice between two uniformly and independently chosen neighbours. We call this process the choice random walk (CRW). We first prove that for any graph, there is a strategy for the CRW that visits any given vertex in expected time ?( E ). Then we introduce a general tool that quantifies by how much the probability of a rare event in the simple random walk can be boosted under a suitable CRW strategy. We believe this result to be of independent interest, and apply it here to derive an almost optimal ?(n log log n) bound for the cover time of bounded-degree expanders. This tool also applies to so-called biased walks, and allows us to make progress towards a conjecture of Azar et al. [STOC 1992]. Finally, we prove the following dichotomy: computing an optimal strategy to minimise the hitting time of a vertex takes polynomial time, whereas computing one to minimise the cover time is NP-hard.
关键词：Power of Two Choices; Markov Chains; Random Walks; Cover Time; Markov Decision Processes