Published March 6, 2009 | Version v1
Journal article

A fast ergodic algorithm for generating ensembles of equilateral random polygons

  • 1. Department of Computer Science, San Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132 (United States)
  • 2. Department of Mathematics and Statistics, University of North Carolina at Charlotte Charlotte, NC 28223 (United States)
  • 3. Department of Mathematics, San Francisco State University, 1600 Holloway Ave, San Francisco, CA 94132 (United States)

Description

Knotted structures are commonly found in circular DNA and along the backbone of certain proteins. In order to properly estimate properties of these three-dimensional structures it is often necessary to generate large ensembles of simulated closed chains (i.e. polygons) of equal edge lengths (such polygons are called equilateral random polygons). However finding efficient algorithms that properly sample the space of equilateral random polygons is a difficult problem. Currently there are no proven algorithms that generate equilateral random polygons with its theoretical distribution. In this paper we propose a method that generates equilateral random polygons in a 'step-wise uniform' way. We prove that this method is ergodic in the sense that any given equilateral random polygon can be generated by this method and we show that the time needed to generate an equilateral random polygon of length n is linear in terms of n. These two properties make this algorithm a big improvement over the existing generating methods. Detailed numerical comparisons of our algorithm with other widely used algorithms are provided.

Availability note (English)

Available from http://dx.doi.org/10.1088/1751-8113/42/9/095204

Additional details

Identifiers

DOI
10.1088/1751-8113/42/9/095204;
PII
S1751-8113(09)98498-0;

Publishing Information

Journal Title
Journal of Physics. A, Mathematical and Theoretical (Online)
Journal Volume
42
Journal Issue
9
Journal Page Range
[14 p.]
ISSN
1751-8121

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
52020784
Subject category
S71: CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS;
Descriptors DEI
ALGORITHMS; COMPARATIVE EVALUATIONS; DNA; PROTEINS; RANDOMNESS; SIMULATION; SPATIAL DISTRIBUTION; THREE-DIMENSIONAL CALCULATIONS
Descriptors DEC
DISTRIBUTION; EVALUATION; MATHEMATICAL LOGIC; NUCLEIC ACIDS; ORGANIC COMPOUNDS