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Stochastic optimization on continuous domains with finite-time guarantees by Markov chain Monte Carlo methods

Author(s):

A. Lecchini, J. Lygeros, Jan M. Maciejowski
Conference/Journal:

IEEE Transactions on Automatic Control, vol. 55, pp. 2858-2863
Abstract:

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 theor

Year:

2010
Type of Publication:

(01)Article
Supervisor:



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% Autogenerated BibTeX entry
@Article { LecLyg:2010:IFA_3702,
    author={A. Lecchini and J. Lygeros and Jan M. Maciejowski},
    title={{Stochastic optimization on continuous domains with
	  finite-time guarantees by Markov chain Monte Carlo methods}},
    journal={IEEE Transactions on Automatic Control},
    year={2010},
    volume={55},
    number={},
    pages={2858--2863},
    month=dec,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=3702}
}
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