Estimating the Mixing Time of Ergodic Markov Chains.

We address the problem of estimating the mixing time $t_{mathsf{mix}}$ of an arbitrary ergodic finite Markov chain from a single trajectory of length $m$. The reversible case was addressed by Hsu et al. [2017], who left the general case as an open problem. In the reversible case, the analysis is greatly facilitated by the fact that the Markov operator is self-adjoint, and Weylu0027s inequality allows for a dimension-free perturbation analysis of the empirical eigenvalues. As Hsu et al. point out, in the absence of reversibility (and hence, the non-symmetry of the pair probabilities matrix), the existing perturbation analysis has a worst-case exponential dependence on the number of states $d$. Furthermore, even if an eigenvalue perturbation analysis with better dependence on $d$ were available, in the non-reversible case the connection between the spectral gap and the mixing time is not nearly as straightforward as in the reversible case. Our key insight is to estimate the pseudo-spectral gap instead, which allows us to overcome the loss of self-adjointness and to achieve a polynomial dependence on $d$ and the minimal stationary probability $pi_star$. Additionally, in the reversible case, we obtain simultaneous nearly (up to logarithmic factors) minimax rates in $t_{mathsf{mix}}$ and precision $varepsilon$, closing a gap in Hsu et al., who treated $varepsilon$ as constant in the lower bounds. Finally, we construct fully empirical confidence intervals for the pseudo-spectral gap, which shrink to zero at a rate of roughly $1/sqrt m$, and improve the state of the art in even the reversible case.