Rapid Mixing of $k$-Class Biased Permutations

Sarah Miracle, Amanda Pascoe Streib

In this paper, we study a biased version of the nearest-neighbor transposition Markov chain on the set of permutations where neighboring elements $i$ and $j$ are placed in order $(i,j)$ with probability $p_{i,j}$. Our goal is to identify the class of parameter sets ${\bf P} = \{p_{i,j}\}$ for which this Markov chain is rapidly mixing. Specifically, we consider the open conjecture of Jim Fill that all monotone, positively biased distributions are rapidly mixing. We resolve Fill's conjecture in the affirmative for distributions arising from $k$-class particle processes, where the elements are divided into $k$ classes and the probability of exchanging neighboring elements depends on the particular classes the elements are in. We further require that $k$ is a constant, and all probabilities between elements in different classes are bounded away from $1/2$. These particle processes arise in the context of self-organizing lists and our result also applies beyond permutations to the setting where all particles in a class are indistinguishable. Additionally we show that a broader class of distributions based on trees is also rapidly mixing, which generalizes a class analyzed by Bhakta et. al. (SODA '13). Our work generalizes recent work by Haddadan and Winkler (STACS '17) studying 3-class particle processes. Our proof involves analyzing a generalized biased exclusion process, which is a nearest-neighbor transposition chain applied to a 2-particle system. Biased exclusion processes are of independent interest, with applications in self-assembly. We generalize the results of Greenberg et al. (SODA '09) and Benjamini et. al (Trans. AMS '05) on biased exclusion processes to allow the probability of swapping neighboring elements to depend on the entire system, as long as the minimum bias is bounded away from $1$.

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