Stochastic optimization on continuous domains with finite-time guarantees by Markov chain Monte Carlo methods
Lecchini-Visintini A. and Lygeros, J. and Maciejowski, J.M.
IEEE Trans. Automatic Control, Volume 55, Number 12, Pages 2858 - 2863, December 2010
DOI: 10.1109/TAC.2010.2078170
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 theory.
Pre-Prints
[PDF]
BibTex Entry
- @Article{,
- author = {Lecchini-Visintini A. and Lygeros, J. and Maciejowski, J.M.},
- journal = {IEEE Trans. Automatic Control},
- title = {Stochastic optimization on continuous domains with finite-time guarantees by Markov chain Monte Carlo methods},
- year = {2010},
- month = {December},
- note = {DOI: 10.1109/TAC.2010.2078170},
- number = {12},
- pages = {2858 - 2863},
- volume = {55}
- }
|
![[Dept of Engineering]](http://www.eng.cam.ac.uk/images/house_style/engban-s.gif)
