Continuous-Time Classical and Quantum Random Walk on Direct Product of Cayley Graphs

In this paper we define direct product of graphs and give a recipe for obtaining probability of observing particle on vertices in the continuous-time classical and quantum random walk. In the recipe, the probability of observing particle on direct product of graph is obtained by multiplication of probability on the corresponding to sub-graphs, where this method is useful to determining probability of walk on compficated graphs. Using this method, we calculate the probability of Continuous-time classical and quantum random walks on many of finite direct product Cayley graphs （complete cycle, complete Kn, charter and n-cube）. Also, we inquire that the classical state the stationary uniform distribution is reached as t→∞ but for quantum state is not always satisfied.     

Bilocal Dynamics for Self-Avoiding Walks

We introduce several bilocal algorithms for lattice self-avoiding walks. We discuss their ergodicity in different confined geometries, for instance in strips and in slabs. A short discussion of the dynamical properties in the absence of interactions is given.     

General Random Walk in One Dimension and Its Application to DNA Transcription Process

We have derived a two-order difference equation about the general random walk in one dimension. Using this model in some physiological processes, we have calculated the average time in which enzyme released at random anywhere moves along a one-dimensional macromolecular strand to the end in quest of its binding site. From the results we can see that the average time can be affected greatly owing to the presence of "obstacles" on the macromolecular strand.     

Random walk on distant mesh points Monte Carlo methods

A new technique for obtaining Monte Carlo algorithms based on the Markov chains with a finite number of states is suggested. Instead of the classical random walk on neighboring mesh points, a general way of constructing Monte Carlo algorithms that could be called random walk on distant mesh points is considered. It is applied to solve boundary value problems. The numerical examples indicate that the new methods are less laborious and therefore more efficient.In conclusion, we mention that all Monte Carlo algorithms are parallel and could be easily realized on parallel computers.     

High-precision Monte Carlo test of the conformai-invariance predictions for two-dimensional mutually avoiding walks

Let _l be the critical exponent associated with the probability thatl independentN-step ordinary random walks, starting at nearby points, are mutually avoiding. Using Monte Carlo methods combined with a maximum-likelihood data analysis, we find that in two dimensions ₂=0.6240±0.0005±0.0011 and ₃=1.4575±0.0030±0.0052, where the first error bar represents systematic error due to corrections to scaling (subjective 95% confidence limits) and the second error bar represents statistical error (classical 95% confidence limits). These results are in good agreement with the conformal-invariance predictions ₂=5/8 and ₃=35/24.     

Monte Carlo generation of self-avoiding walks with fixed endpoints and fixed length

We propose a new class of dynamic Monte Carlo algorithms for generating self-avoiding walks uniformly from the ensemble with fixed endpoints and fixed length in any dimension, and prove that these algorithms are ergodic in all cases. We also prove the ergodicity of a variant of the pivot algorithm.     

On Origin of Power-Law Distributions in Self-Organized Criticality from Random Walk Treatment

The origin of power-law distributions in self-organized criticality is investigated by treating the variation of the number of active sites in the system as a stochastic process. An avalanche is then regarded as a first-return random walk process in a one-dimensional lattice. We assume that the variation of the number of active sites has three possibilities in each update： to increase by 1 with probability f1, to decrease by 1 with probability f2, or remain unchanged with probability 1 - f1 - f2. This mimics the dynamics in the system. Power-law distributions of the lifetime are found when the random walk is unbiased with equal probability to move in opposite directions. This shows that power-law distributions in self-organized criticality may be caused by the balance of competitive interactions.     

Exponential convergence to equilibrium for a class of random-walk models

We prove exponential convergence to equilibrium (L ² geometric ergodicity) for a random walk with inward drift on a sub-Cayley rooted tree. This randomwalk model generalizes a Monte Carlo algorithm for the self-avoiding walk proposed by Berretti and Sokal. If the number of vertices of levelN in the tree grows asC _N ~ ^N N ^–1 , we prove that the autocorrelation time satisfies N² N¹⁺     

Cut-and-Permute Algorithm for Self-Avoiding Walks in the Presence of Surfaces

We present a dynamic nonlocal hybrid Monte Carlo algorithm consisting of pivot and cut-and-permute moves. The algorithm is suitable for the study of polymers in semiconfined geometries at the ordinary transition, where the pivot algorithm exhibits quasi-ergodic problems. The dynamic properties of the proposed algorithm are studied in d=3. The hybrid dynamics is ergodic and exhibits the same optimal critical behavior as the pivot algorithm in the bulk.     

A Note on Transience Versus Recurrence for a Branching Random Walk in Random Environment

We consider a branching random walk in random environment on ^d where particles perform independent simple random walks and branch, according to a given offspring distribution, at a random subset of sites whose density tends to zero at infinity. Given that initially one particle starts at the origin, we identify the critical rate of decay of the density of the branching sites separating transience from recurrence, i.e., the progeny hits the origin with probability <1 resp. =1. We show that for d3 there is a dichotomy in the critical rate of decay, depending on whether the mean offspring at a branching site is above or below a certain value related to the return probability of the simple random walk. The dichotomy marks a transition from local to global behavior in the progeny that hits the origin. We also consider the situation where the branching sites occur in two or more types, with different offspring distributions, and show that the classification is more subtle due to a possible interplay between the types. This note is part of a series of papers by the second author and various co-authors investigating the problem of transience versus recurrence for random motions in random media.     

基于改进的压缩式随机游走算法对静态电源/地网的模拟

将随机游走法和等效电路压缩相结合,对静态P/G网(Power and Ground Networks)进行分析.针对一个大规模的电路,在经过多层的参数提取和建模后,得到静态P/G网模型.首先根据网络的规律性,运用等效电路压缩法将原始的P/G网进行压缩处理,然后运用随机游走法求解,最后利用计算得到的化简网络电压值,通过相关的插值公式得到原网络的电压值.实验数据表明,改进的压缩式随机游走法可有效简化网络的复杂性,节省计算时间,计算速度提高到普通随机游走法的两个数量级以上.     

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.     

基于强制进化随机游走算法的质量交换网络综合

针对现有质量交换网络优化方法存在的不足, 提出一种适用于质量交换网络综合的强制进化随机游走算法。通过随机增大或减小质量交换器的传质负荷、分流比以及贫流股的流量, 并设置最小阈值实现网络连续和整型变量的同步优化; 通过以较小概率接受差解, 增强结构变异, 使算法更好地兼顾质量交换网络的全局和局部搜索。在2个质量交换网络实例中的应用表明, 优化结果优于文献最优结果, 算法保持了个体的独立进化且具有良好的全局和局部搜索能力。     

采用单元进化限制策略的RWCE算法优化换热网络

针对强制进化随机游走算法（RWCE）优化换热网络时后期进化缓慢的不足,提出一种采用单元进化限制策略的改进RWCE算法（RS-RWCE）优化换热网络.通过限制每次迭代个体中参与进化的换热单元数,在保持进化前期年综合费用收敛速度较快的同时,保证进化后期的精细搜索能力.通过对两个经典算例的比较验证,证明改进后的算法应用于换热网络优化问题时兼顾了整型变量与连续变量的优化,使计算效率和精度均得到提高,同时获得了较好的换热网络优化结果.     

Continuous Time Random Walk with Correlated Waiting Times. The Crucial Role of Inter-Trade Times in Volatility Clustering

In many physical, social, and economic phenomena, we observe changes in a studied quantity only in discrete, irregularly distributed points in time. The stochastic process usually applied to describe this kind of variable is the continuous-time random walk (CTRW). Despite the popularity of these types of stochastic processes and strong empirical motivation, models with a long-term memory within the sequence of time intervals between observations are rare in the physics literature. Here, we fill this gap by introducing a new family of CTRWs. The memory is introduced to the model by assuming that many consecutive time intervals can be the same. Surprisingly, in this process we can observe a slowly decaying nonlinear autocorrelation function without a fat-tailed distribution of time intervals. Our model, applied to high-frequency stock market data, can successfully describe the slope of decay of the nonlinear autocorrelation function of stock market returns. We achieve this result without imposing any dependence between consecutive price changes. This proves the crucial role of inter-event times in the volatility clustering phenomenon observed in all stock markets.     

The Critical Attractive Random Polymer in Dimension One

A polymer chain with attractive and repulsive forces between the building blocks is modeled by attaching a weight e ^– for every self-intersection and e ^/(2d) for every self-contact to the probability of an n-step simple random walk on ^d , where , >0 are parameters. It is known that for d=1 and > the chain collapses down to finitely many sites, while for d=1 and < it spreads out ballistically. Here we study for d=1 the critical case = corresponding to the collapse transition and show that the end-to-end distance runs on the scale _n = (log n)^–1/4. We describe the asymptotic shape of the accordingly scaled local times in terms of an explicit variational formula and prove that the scaled polymer chain occupies a region of size _n times a constant. Moreover, we derive the asymptotics of the partition function.     

采用三层保护策略的强制进化随机游走算法同步综合换热网络

强制进化随机游走算法（RWCE）同步综合换热网络时,存在个体最优解的进化路径被接受差解打乱而不接受差解又很难跳出局部最优的问题.提出一种采用三层保护策略的RWCE算法,将种群中个体分为三层,底层采用基本RWCE进行优化,以保护个体的全局搜索能力;中层读取底层各个体的历史最优解,并采用带微调功能的RWCE进行优化,以保护各个体最优解的进化路径不被打乱;顶层所有个体以中层最优个体的解为初始点,采用带自动精细搜索功能的RWCE进行优化,以保证最优个体得到充分的搜索;最后将顶层搜索到的结果传递给底层对应个体.实例表明,算法在允许接受差解的同时保护了个体最优解的进化路径,并实现了全局搜索能力与局部搜索能力的兼顾.     

随机级联模型的连续标度行为

将传统的阶乘矩统计方法推广到分割数或相空间元胞大小连续变化的情形,发现以往元胞大小分立变化的随机级联α模型存在＂链条效应＂,没有连续的标度率.考虑到实验过程及实际测量情况,对模型进行了切合实际的改进,让相空间的大小和位置随机起伏.这样得到的分形体,在相空间范围的起伏足够大时,有较好的标度不变性.能达到这一要求的相空间范围起伏的临界值,对模型参数α不敏感.用Monte Carlo模拟证明了,相空间范围的起伏对模型的反常标度指数影响不大.     

On the Distribution of Surface Extrema in Several One- and Two-dimensional Random Landscapes

We study here a standard next-nearest-neighbor (NNN) model of ballistic growth on one-and two-dimensional substrates focusing our analysis on the probability distribution function P(M,L) of the number M of maximal points (i.e., local “peaks”) of growing surfaces. Our analysis is based on two central results: (i) the proof (presented here) of the fact that uniform one-dimensional ballistic growth process in the steady state can be mapped onto “rise-and-descent” sequences in the ensemble of random permutation matrices; and (ii) the fact, established in Ref. [G. Oshanin and R. Voituriez, J. Phys. A: Math. Gen. 37:6221 (2004)], that different characteristics of “rise-and-descent” patterns in random permutations can be interpreted in terms of a certain continuous-space Hammersley-type process. For one-dimensional system we compute P(M,L) exactly and also present explicit results for the correlation function characterizing the enveloping surface. For surfaces grown on 2d substrates, we pursue similar approach considering the ensemble of permutation matrices with long-ranged correlations. Determining exactly the first three cumulants of the corresponding distribution function, we define it in the scaling limit using an expansion in the Edgeworth series, and show that it converges to a Gaussian function as L → ∞.     

Photon transport in thin disordered slabs

We examine using Monte Carlo simulations, photon transport in optically ‘thin’ slabs whose thickness L is only a few times the transport mean free path l*, with particles of different scattering anisotropies. The confined geometry causes an auto-selection of only photons with looping paths to remain within the slab. The results of the Monte Carlo simulations are borne out by our analytical treatment that incorporates directional persistence by the use of the Ornstein-Uhlenbeck process, which interpolates between the short time ballistic and long time diffusive regimes.