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Stochastic Optimization on Continuous Domains With Finite-Time Guarantees by Markov Chain Monte Carlo Methods

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3 Author(s)
Lecchini-Visintini, A. ; Dept. of Eng., Univ. of Leicester, Leicester, UK ; Lygeros, J. ; Maciejowski, J.M.

We introduce bounds on the finite-time performance of Markov chain Monte Carlo (MCMC) algorithms in solving global stochastic optimization problems defined over continuous domains. It is shown that MCMC algorithms with finite-time guarantees can be developed with a proper choice of the target distribution and by studying their convergence in total variation norm. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.

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Automatic Control, IEEE Transactions on  (Volume:55 ,  Issue: 12 )