Corpus ID: 124483802

Asymptotic and non-asymptotic convergence properties of stochastic approximation with controlled Markov noise using lock-in probability

@inproceedings{Karmakar2016AsymptoticAN,
  title={Asymptotic and non-asymptotic convergence properties of stochastic approximation with controlled Markov noise using lock-in probability},
  author={Prasenjit Karmakar and Arunselvan Ramaswamy and Shalabh Bhatnagar and Vivek S. Borkar},
  year={2016}
}
  • Prasenjit Karmakar, Arunselvan Ramaswamy, +1 author Vivek S. Borkar
  • Published 2016
  • Mathematics
  • This paper talks about both the asymptotic and non-asymptotic convergence properties of stochastic approximation algorithms with controlled Markov noise by extending the well known lock-in probability framework for such recursions. Here we give a lower bound on the lock-in probability of such frameworks i.e. the probability of convergence to a specific attractor of the o.d.e. limit given that the iterates visit its domain of attraction after a sufficiently large number of iterationsn0. With the… CONTINUE READING

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