On Continuous-Time Self-Avoiding Random Walk in Dimension Four

We study the distribution of the end-to-end distance of continuous-time self-avoiding random walks (CTRW) in dimension four from two viewpoints. From a real-space renormalization-group map on probabilities, we conjecture the asymptotic behavior of the end-to-end distance of a weakly self-avoiding random walk (SARW) that penalizes two-body interactions of random walks in dimension four on a hierarchical lattice. Then we perform the Monte Carlo computer simulations of CTRW on the four-dimensional integer lattice, paying special attention to the difference in statistical behavior of the CTRW compared with the discrete-time random walks. In this framework, we verify the result already predicted by the renormalization-group method and provide new results related to enumeration of self-avoiding random walks and calculation of the mean square end-to-end distance and gyration radius of continous-time self-avoiding random walks.     

On the coordination number of self-avoiding two-dimensional random walks

A numerical value for the effective coordination number of self-avoiding random walks has recently been obtained by analytical considerations. This value differs considerably from the empirical value obtained by computer simulations. It turns out that a seemingly severe simplification in the analytical approach is not the first cause of this discrepancy.     

The effective coordination number of self-avoiding two-dimensional random walks

It is demonstrated that the effective coordination number of self-avoiding random walks in a plane is given by exp(−π²/24) = 0.66283.     

Novel quantum phases and mesoscopic physics in quantum gases

Among many notable jubilees brought by the year 2012, the one of a special importance for the community of statistical physicists was the 140th birth anniversary of Marian Smoluchowski (Maryan Ritter von Smolan Smoluchowski, 28.05.1872 - 5.09.1917), who was one of the pioneers of statistical physics and, on a larger scale, one of those who shaped modern physical science as a whole. The present issue of EPJ ST entitled From Brownian motion to self-avoiding walks and Lévy flights aims to reflect the evolution of Smoluchowski’s ideas in the field of statistics of interacting random and self-avoiding walks, stochastic equations for many-particle systems, physics of glass-forming and noise driven systems. Majority of papers in this issue were presented at the international conference in statistical physics that took place in Lviv (Ukraine) on July 3-6, 2012.     

Self-avoiding walks on strongly diluted lattices: Chain-growth simulations vs. exact enumeration

We discuss and compare two different methods that can be used to investigate self-avoiding walks on lattices with random site dilution. One is the so-called pruned-enriched Rosenbluth method (PERM), a chain-growth Monte Carlo algorithm, the other is a recently developed exact enumeration technique. While the latter is highly efficient for systems close to the critical concentration, it cannot be used for less dilute systems. PERM is more versatile but appears to have difficulties coping with strong confinement.     

Random walks with short-range interaction and mean-field behavior

We introduce a model of self-repelling random walks where the short-range interaction between two elements of the chain decreases as a power of the difference in proper time. The model interpolates between the lattice Edwards model and the ordinary random walk. We show by means of Monte Carlo simulations in two dimensions that the exponentv _MF obtained through a mean-field approximation correctly describes the numerical data and is probably exact as long as it is smaller than the corresponding exponent for self-avoiding walks. We also compute the exponent and present a numerical study of the scaling functions.     

The Upper Critical Dimension of the Abelian Sandpile Model

The existing estimation of the upper critical dimension of the Abelian Sandpile Model is based on a qualitative consideration of avalanches as self-avoiding branching processes. We find an exact representation of an avalanche as a sequence of spanning subtrees of two-component spanning trees. Using equivalence between chemical paths on the spanning tree and loop-erased random walks, we reduce the problem to determination of the fractal dimension of spanning subtrees. Then the upper critical dimension d _u=4 follows from Lawler's theorems for intersection probabilities of random walks and loop-erased random walks.     

New exponent in self-avoiding walks

A new exponent is reported in the problem of non-intersecting self-avoiding random walks. It is connected with the asymptotic behaviour of the growth of number of such walks. The value of the exponent is found to be nearly 0.90 for all two dimensional and nearly 0.96 for all three dimensional, lattices studied here. It approaches the value 1.0 assymptotically as the dimensionality approaches infinity.     

Accessibility in complex networks

This Letter describes a method for the quantification of the diversity of non-linear dynamics in complex networks as a consequence of self-avoiding random walks. The methodology is analyzed in the context of theoretical models and illustrated with respect to the characterization of the accessibility in urban streets.     

Asymptotic results on occupancy times of random walks

In this note we derive, using Wald's theorem asymptotic results on mean occupancy time of an interval for random walks with arbitrary transition probabilities. We show that our results are consistent with those obtained (by Weiss, Ref. 2) via the master equation approach, by demonstrating that the resulting infinite series can be summed exactly.     

Asymptotic distributions for self-avoiding walks constrained to strips,cylinders, and tubes

A transfer matrix method for treating self-avoiding walks on a lattice is developed. Single walks confined to infinitely long strips, cylinders, or tubes are considered, particularly in the limit where the length of the walk becomes infinite compared to the transverse dimensions. In this case relevant distributions are demonstrated to be asymptotically Gaussian. Explicit numerical results are given for a few of the narrower systems. Similar results for self-avoiding cycles are indicated, too. Finally, the behavior of the various distributions as a function of strip width is discussed.Supported by the Robert A. Welch Foundation, Houston, Texas.     

Open Quantum Random Walks

A new model of quantum random walks is introduced, on lattices as well as on finite graphs. These quantum random walks take into account the behavior of open quantum systems. They are the exact quantum analogues of classical Markov chains. We explore the “quantum trajectory” point of view on these quantum random walks, that is, we show that measuring the position of the particle after each time-step gives rise to a classical Markov chain, on the lattice times the state space of the particle. This quantum trajectory is a simulation of the master equation of the quantum random walk. The physical pertinence of such quantum random walks and the way they can be concretely realized is discussed. Differences and connections with the already well-known quantum random walks, such as the Hadamard random walk, are established.     

Walks on Apollonian networks

We carry out comparative studies of random walks on deterministic Apollonian networks (DANs) and random Apollonian networks (RANs). We perform computer simulations for the mean first-passage time, the average return time, the mean-square displacement, and the network coverage for the unrestricted random walk. The diffusions both on DANs and RANs are proved to be sublinear. The effects of the network structure on the dynamics and the search efficiencies of walks with various strategies are also discussed. Contrary to intuition, it is shown that the self-avoiding random walk, which has been verified as an optimal local search strategy in networks, is not the best strategy for the DANs in the large size limit.     

Group theory,Markov chains,and excluded volume effect in polymers

A restricted walk of orderr on a lattice is defined as a random walk in which polygons withr vertices or less are excluded. A study of restricted walks for increasingr provides an understanding of how the transition in properties is effected from random to self-avoiding walks which is important in our understanding of the excluded volume effect in polymers and in the study of many other problems. Here the properties of restricted walks are studied by the transition matrix method based on the theory of Markov chains. A group theoretical method is used to reduce the transition matrix governing the walk in a systematic manner and to classify the eigenvalues of the transition matrix according to the various representations of the appropriate group. It is shown that only those eigenvalues corresponding to two particular representations of the group contribute to the correlations among the steps of the walk. The distributions of eigenvalues for walks of various ordersr on the two-dimensional triangular lattice and the three-dimensional face-centered cubic lattice are presented, and they are shown to have some remarkable features.     

Adsorption of a flexible self-avoiding polymer chain: Exact results on fractal lattices

The large-scale behavior of surface-interacting self-avoiding polymer chains placed on finitely ramified fractal lattices is studied using exact recursion relations. It is shown how to obtain surface susceptibility critical indices and how to modify a scaling relation for these indices in the case of fractal lattices. We present the exact results for critical exponents at the point of adsorption transition for polymer chains situated on a class of Sierpinski gasket-type fractals. We provide numerical evidence for a critical behavior of the type found recently in the case of bulk self-avoiding random walks at the fractal to Euclidean crossover.     

Testing the hyperscaling relation for self-avoiding walks in three dimensions

We test numerically the hyperscaling relation involving the critical exponent Δ₄ (associated with the four-point function) for self-avoiding random walks in three dimensions. We argue that this relation is satisfied with good accuracy.     

Correction-to-Scaling Exponents for Two-Dimensional Self-Avoiding Walks

We study the correction-to-scaling exponents for the two-dimensional self-avoiding walk, using a combination of series-extrapolation and Monte Carlo methods. We enumerate all self-avoiding walks up to 59 steps on the square lattice, and up to 40 steps on the triangular lattice, measuring the mean-square end-to-end distance, the mean-square radius of gyration and the mean-square distance of a monomer from the endpoints. The complete endpoint distribution is also calculated for self-avoiding walks up to 32 steps (square) and up to 22 steps (triangular). We also generate self-avoiding walks on the square lattice by Monte Carlo, using the pivot algorithm, obtaining the mean-square radii to ≈ 0.01% accuracy up to N=4000. We give compelling evidence that the first non-analytic correction term for two-dimensional self-avoiding walks is Δ₁=3/2. We compute several moments of the endpoint distribution function, finding good agreement with the field-theoretic predictions. Finally, we study a particular invariant ratio that can be shown, by conformal-field-theory arguments, to vanish asymptotically, and we find the cancellation of the leading analytic correction.     

Efficient Implementation of the Pivot Algorithm for Self-avoiding Walks

The pivot algorithm for self-avoiding walks has been implemented in a manner which is dramatically faster than previous implementations, enabling extremely long walks to be efficiently simulated. We explicitly describe the data structures and algorithms used, and provide a heuristic argument that the mean time per attempted pivot for N-step self-avoiding walks is O(1) for the square and simple cubic lattices. Numerical experiments conducted for self-avoiding walks with up to 268 million steps are consistent with o(log N) behavior for the square lattice and O(log N) behavior for the simple cubic lattice. Our method can be adapted to other models of polymers with short-range interactions, on the lattice or in the continuum, and hence promises to be widely useful.     

Kinetic-growth self-avoiding walks on small-world networks

Kinetically-grown self-avoiding walks have been studied on Watts-Strogatz small-world networks, rewired from a two-dimensional square lattice. The maximum length L of this kind of walks is limited in regular lattices by an attrition effect, which gives finite values for its mean value 〈L 〉. For random networks, this mean attrition length 〈L 〉 scales as a power of the network size, and diverges in the thermodynamic limit (system size N ↦∞). For small-world networks, we find a behavior that interpolates between those corresponding to regular lattices and randon networks, for rewiring probability p ranging from 0 to 1. For p < 1, the mean self-intersection and attrition length of kinetically-grown walks are finite. For p = 1, 〈L 〉 grows with system size as N^1/2, diverging in the thermodynamic limit. In this limit and close to p = 1, the mean attrition length diverges as (1-p)^-4. Results of approximate probabilistic calculations agree well with those derived from numerical simulations.     

Combinatorial and approximative analyses in a spatially random division process

For a spatial characteristic, there exist commonly fat-tail frequency distributions of fragment-size and -mass of glass, areas enclosed by city roads, and pore size/volume in random packings. In order to give a new analytical approach for the distributions, we consider a simple model which constructs a fractal-like hierarchical network based on random divisions of rectangles. The stochastic process makes a Markov chain and corresponds to directional random walks with splitting into four particles. We derive a combinatorial analytical form and its continuous approximation for the distribution of rectangle areas, and numerically show a good fitting with the actual distribution in the averaging behavior of the divisions.