Indexing metadata

Diffusive Long-time Behavior of Kawasaki Dynamics


 
Dublin Core PKP Metadata Items Metadata for this Document
 
1. Title Title of document Diffusive Long-time Behavior of Kawasaki Dynamics
 
2. Creator Author's name, affiliation, country Nicoletta Cancrini; Università dell'Aquila, Italy
 
2. Creator Author's name, affiliation, country Filippo Cesi; Università di Roma ``La Sapienza", Italy
 
2. Creator Author's name, affiliation, country Cyril Roberto; Università di Roma 3, Italy
 
3. Subject Discipline(s)
 
3. Subject Keyword(s)
 
4. Description Abstract If $P_t$ is the semigroup associated with the Kawasaki dynamics on $Z^d$ and $f$ is a local function on the configuration space, then the variance with respect to the invariant measure $\mu$ of $P_t f$ goes to zero as $t\to \infty$ faster than $t^{-d/2+\varepsilon}$, with $\varepsilon$ arbitrarily small. The fundamental assumption is a mixing condition on the interaction of Dobrushin and Schlosman type.
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 2005-03-04
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier http://ejp.ejpecp.org/article/view/239
 
10. Identifier Digital Object Identifier 10.1214/EJP.v10-239
 
11. Source Journal/conference title; vol., no. (year) Electronic Journal of Probability; Vol 10
 
12. Language English=en
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
15. Rights Copyright and permissions The Electronic Journal of Probability applies the Creative Commons Attribution License (CCAL) to all articles we publish in this journal. Under the CCAL, authors retain ownership of the copyright for their article, but authors allow anyone to download, reuse, reprint, modify, distribute, and/or copy articles published in EJP, so long as the original authors and source are credited. This broad license was developed to facilitate open access to, and free use of, original works of all types. Applying this standard license to your work will ensure your right to make your work freely and openly available.

Summary of the Creative Commons Attribution License

You are free
  • to copy, distribute, display, and perform the work
  • to make derivative works
  • to make commercial use of the work
under the following condition of Attribution: others must attribute the work if displayed on the web or stored in any electronic archive by making a link back to the website of EJP via its Digital Object Identifier (DOI), or if published in other media by acknowledging prior publication in this Journal with a precise citation including the DOI. For any further reuse or distribution, the same terms apply. Any of these conditions can be waived by permission of the Corresponding Author.