Rapidly Mixing Markov Chains: A Comparison of Techniques (A Survey)
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Abstract (arXiv)
We survey existing techniques to bound the mixing time of Markov chains. The mixing time is related to a geometric parameter called conductance which is a measure of edge-expansion. Bounds on conductance are typically obtained by a technique called "canonical paths" where the idea is to find a set of paths, one between every source-destination pair, such that no edge is heavily congested. However, the canonical paths approach cannot always show rapid mixing of a rapidly mixing chain. This drawback disappears if we allow the flow between a pair of states to be spread along multiple paths. We prove that for a large class of Markov chains canonical paths does capture rapid mixing. Allowing multiple paths to route the flow still does help a great deal in proofs, as illustrated by a result of Morris & Sinclair (FOCS'99) on the rapid mixing of a Markov chain for sampling 0/1 knapsack solutions. A different approach to prove rapid mixing is "Coupling". Path Coupling is a variant discovered by Bubley & Dyer (FOCS'97) that often tremendously reduces the complexity of designing good Couplings. We present several applications of Path Coupling in proofs of rapid mixing. These invariably lead to much better bounds on mixing time than known using conductance, and moreover Coupling based proofs are typically simpler. This motivates the question of whether Coupling can be made to work whenever the chain is rapidly mixing. This question was answered in the negative by Kumar & Ramesh (FOCS'99), who showed that no Coupling strategy can prove the rapid mixing of the Jerrum-Sinclair chain for sampling perfect and near-perfect matchings.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Mar 2016.
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Citing papers per year: 2024: 1; 2025: 3.
4 papers.
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- 2025Disappearing Ink: Obfuscation Breaks N-gram Code Watermarks in Theory and Practice7 Jul 2025
- 2025Replicable Distribution Testing3 Jul 2025
- 2025A Tale of Two Learning Algorithms: Multiple Stream Random Walk and Asynchronous Gossip14 Apr 2025
- 2024Dimension-free Relaxation Times of Informed MCMC Samplers on Discrete Spaces5 Apr 2024