Large deviations for a class of nonhomogeneous Markov chains

Zach Dietz, Sunder Sethuraman

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Large deviation results are given for a class of perturbed nonhomogeneous Markov chains on finite state space which formally includes some stochastic optimization algorithms. Specifically, let {P n} be a sequence of transition matrices on a finite state space which converge to a limit transition matrix P. Let [X n] be the associated nonhomogeneous Markov chain where P n controls movement from time n - 1 to n. The main statements are a large deviation principle and bounds for additive functionals of the nonhomogeneous process under some regularity conditions. In particular, when P is reducible, three regimes that depend on the decay of certain "connection" P n probabilities are identified. Roughly, if the decay is too slow, too fast or in an intermediate range, the large deviation behavior is trivial, the same as the time-homogeneous chain run with P or nontrivial and involving the decay rates. Examples of anomalous behaviors are also given when the approach P n → P is irregular. Results in the intermediate regime apply to geometrically fast running optimizations, and to some issues in glassy physics.

Original languageEnglish (US)
Pages (from-to)421-486
Number of pages66
JournalAnnals of Applied Probability
Issue number1 A
StatePublished - Feb 2005


  • Geometric cooling
  • Glassy models
  • Large deviations
  • Markov
  • Nonhomogeneous
  • Optimization

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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