THE SYMPLECTIC GEOMETRY OF CLOSED EQUILATERAL RANDOM WALKS IN 3-SPACE

• 成果类型: Article
• 署名作者: Cantarella, Jason; Shonkwiler, Clayton
• 署名单位: University System of Georgia; University of Georgia; Colorado State University System; Colorado State University Fort Collins
• 刊物名称: ANNALS OF APPLIED PROBABILITY
• ISSN/ISSBN: 1050-5164
• DOI: 10.1214/15-AAP1100
• 发表日期: 2016
• 页码: 549-596
• 关键词: exploring posterior distributions; zero-one law; hit-and-run; single-molecule; total curvature; dna mechanics; markov-chains; polygons; SPACE; COHOMOLOGY

摘要

A closed equilateral random walk in 3-space is a selection of unit length vectors giving the steps of the walk conditioned on the assumption that the sum of the vectors is zero. The sample space of such walks with n edges is the (2n- 3)-dimensional Riemannian manifold of equilateral closed polygons in R-3. We study closed random walks using the symplectic geometry of the (2n - 6)-dimensional quotient of the manifold of polygons by the action of the rotation group SO(3). The basic objects of study are the moment maps on equilateral random polygon space given by the lengths of any (n - 3)-tuple of nonintersecting diagonals. The Atiyah-Guillemin-Sternberg theorem shows that the image of such a moment map is a convex polytope in (n - 3)-dimensional space, while the Duistermaat-Heckman theorem shows that the pushforward measure on this polytope is Lebesgue measure on Rn-3. Together, these theorems allow us to define a measure-preserving set of action-angle coordinates on the space of closed equilateral polygons. The new coordinate system allows us to make explicit computations of exact expectations for total curvature and for some chord lengths of closed (and confined) equilateral random walks, to give statistical criteria for sampling algorithms on the space of polygons and to prove that the probability that a randomly chosen equilateral hexagon is unknotted is at least 1/2. We then use our methods to construct a new Markov chain sampling algorithm for equilateral closed polygons, with a simple modification to sample (rooted) confined equilateral closed polygons. We prove rigorously that our algorithm converges geometrically to the standard measure on the space of closed random walks, give a theory of error estimators for Markov chain Monte Carlo integration using our method and analyze the performance of our method. Our methods also apply to open random walks in certain types of confinement, and in general to walks with arbitrary (fixed) edgelengths as well as equilateral walks.