Markov Chain Monte Carlo method exploiting barrier functions with applications to control and optimization

B. T. Polyak, E. N. Gryazina

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

In previous works the authors proposed to use Hit-and-Run (H&R) versions of Markov Chain Monte Carlo (MCMC) algorithms for various problems of control and optimization. However the results are unsatisfactory for .bad. sets, such as level sets of ill-posed functions. The idea of the present paper is to exploit the technique developed for interior-point methods of convex optimization, and to combine it with MCMC algorithms. We present a new modification of H&R method exploiting barrier functions and its validation. Such approach is well tailored for sets defined by linear matrix inequalities (LMI), which are widely used in control and optimization. The results of numerical simulation are promising.

Original languageEnglish
Title of host publication2010 IEEE International Symposium on Computer-Aided Control System Design, CACSD 2010
Pages1553-1557
Number of pages5
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 IEEE International Symposium on Computer-Aided Control System Design, CACSD 2010 - Yokohama, Japan
Duration: 8 Sep 201010 Sep 2010

Publication series

NameProceedings of the IEEE International Symposium on Computer-Aided Control System Design

Conference

Conference2010 IEEE International Symposium on Computer-Aided Control System Design, CACSD 2010
Country/TerritoryJapan
CityYokohama
Period8/09/1010/09/10

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