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Upgrading MLSI to LSI for reversible Markov chains

2023; Elsevier BV; Volume: 285; Issue: 9 Linguagem: Inglês

10.1016/j.jfa.2023.110076

1096-0783

Justin Salez, Konstantin Tikhomirov, Pierre Youssef,

Algorithms and Data Compression

For reversible Markov chains on finite state spaces, we show that the modified log-Sobolev inequality (MLSI) can be upgraded to a log-Sobolev inequality (LSI) at the surprisingly low cost of degrading the associated constant by log⁡(1/p), where p is the minimum non-zero transition probability. We illustrate this by providing the first log-Sobolev estimate for Zero-Range processes on arbitrary graphs. As another application, we determine the modified log-Sobolev constant of the Lamplighter chain on all bounded-degree graphs, and use it to provide negative answers to two open questions by Montenegro and Tetali (2006) [27] and Hermon and Peres (2018) [17]. Our proof builds upon the 'regularization trick' recently introduced by the last two authors.