Total Variation Discrepancy of Deterministic Random Walks for Ergodic Markov Chains

Abstract: Motivated by a derandomization of Markov chain Monte Carlo (MCMC), this paper investigates deterministic random walks, which is a deterministic process analogous to a random walk. While there are several progresses on the analysis of the vertex-wise discrepancy (i.e., $L_\infty$ discrepancy), little is known about the {\em total variation discrepancy} (i.e., $L_1$ discrepancy), which plays a significant role in the analysis of an FPRAS based on MCMC. This paper investigates upper bounds of the $L_1$ discrepancy between the expected number of tokens in a Markov chain and the number of tokens in its corresponding deterministic random walk. First, we give a simple but nontrivial upper bound ${\rm O}(mt^*)$ of the $L_1$ discrepancy for any ergodic Markov chains, where $m$ is the number of edges of the transition diagram and $t^*$ is the mixing time of the Markov chain. Then, we give a better upper bound ${\rm O}(m\sqrt{t^*\log t^*})$ for non-oblivious deterministic random walks, if the corresponding Markov chain is ergodic and lazy. We also present some lower bounds.