Optimizing the asymptotic convergence rate of the Diaconis–Holmes–Neal sampler

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We consider the problem of optimizing the asymptotic convergence rate of a parameter-dependent nonreversible Markov chain. We begin with a single-parameter case studied by Diaconis, Holmes and Neal and then introduce multiple parameters. We use nonsmooth analysis to investigate whether the presence of multiple parameters allows a faster asymptotic convergence rate, and argue that for a specific parameterization, it does not, at least locally.