Convergence rates of Markov chains for some self-assembly and non-saturated Ising models
type: Item
instance of: scholarly article
title: Convergence rates of Markov chains for some self-assembly and non-saturated Ising models
zbMATH Open document ID: 1170.60027
author: Sam Greenberg
author: Dana Randall
published in: Theoretical Computer Science
publication date: 2009-03-30T00:00:00Z
review text: The authors define the non-saturated Ising model to represent a simple DNA-based self-assembly model consisting of two types of tiles. It is shown that the assembly is efficient at high temperatures, where the stationary distribution allows many ``errors'' or unfavorable neighbors, and the assembly is slow at low temperatures, where errors are much less likely.
Mathematics Subject Classification ID: 60J10
Mathematics Subject Classification ID: 60K35
Mathematics Subject Classification ID: 60F05
Mathematics Subject Classification ID: 82B20
zbMATH DE Number: 5534947
zbMATH Keywords: Markov chain
zbMATH Keywords: sampling algoruthm
zbMATH Keywords: Ising model
zbMATH Keywords: self-assembly
zbMATH Keywords: phase transition
reviewed by: U. A. Rozikov
MaRDI profile type: Publication
full work available at URL: j.tcs.2008.12.007
OpenAlex ID: W2014980539
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DOI: 10.1016/J.TCS.2008.12.007
description: scientific article; zbMATH DE number 5534947