Control For Energy and Sustainability

EPSRC Programme Grant

[LLM10] A. Lecchini Visintini, J. Lygeros, J.M. Maciejowski, Stochastic Optmization on Continuous Domains with Finite-time Guarantees by Markov Chain Monte Carlo Methods, IEEE Transactions on Automatic Control, Vol 55, 12, pp 2858-2863, Dec., 2010


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.