Model of <Emphasis Type="Italic">p</Emphasis>-Adic Random Walk in a Potential

We consider the p-adic random walk model in a potential which can be viewed as a generalization of p-adic random walk models used for describing protein conformational dynamics. This model is based on the Kolmogorov-Feller equations for the distribution function defined on the field of p-adic numbers in which the transition rate depends on ultrametric distance between the transition points as well as on function of potential violating the spatial homogeneity of a random process. This equation which will be called the equation of p-adic random walk in a potential, is equivalent to the equation of p-adic random walk with modified measure and reaction source. With a special choice of a power-law potential the last equation is shown to have an exact analytic solution. We find the analytic solution of the Cauchy problem for such equation with an initial condition, whose support lies in the ring of integer p-adic numbers.We also examine the asymptotic behaviour of the distribution function for large times. It is shown that in the limit t→∞ the distribution function tends to the equilibrium solution according to the law, which is bounded from above and below by power laws with the same exponent. Our principal conclusion is that the introduction of a potential in the model of p-adic random walk conserves the power-law behaviour of relaxation curves for large times.     

Ultrametric and Tree Potential

In this article we study which infinite matrices are potential matrices. We tackle this problem in the ultrametric framework by studying infinite tree matrices and ultrametric matrices. For each tree matrix, we show the existence of an associated symmetric random walk and study its Green potential. We provide a representation theorem for harmonic functions that includes simple expressions for any increasing harmonic function and the Martin kernel. For ultrametric matrices, we supply probabilistic conditions to study its potential properties when immersed in its minimal tree matrix extension. C. Dellacherie thanks support from Nucleus Millennium P04-069-F for his visit to CMM-DIM at Santiago. The research of S. Martinez is supported by Nucleus Millennium Information and Randomness P04-069-F and by the BASAL CONICYT Program. The research of J. San Martin is supported by FONDAP and by the BASAL CONICYT Program.     

Error estimates for the numerical approximation of Neumann control problems governed by a class of quasilinear elliptic equations

We study the numerical approximation of Neumann boundary optimal control problems governed by a class of quasilinear elliptic equations. The coefficients of the main part of the operator depend on the state function, as a consequence the state equation is not monotone. We prove that strict local minima of the control problem can be approximated uniformly by local minima of discrete control problems and we also get an estimate of the rate of this convergence. One of the main issues in this study is the error analysis of the discretization of the state and adjoint state equations. Some difficulties arise due to the lack of uniqueness of solution of the discrete equations. The theoretical results are illustrated by numerical tests.     

Iterative Convex Quadratic Approximation for Global Optimization in Protein Docking

An algorithm for finding an approximate global minimum of a funnel shaped function with many local minima is described. It is applied to compute the minimum energy docking position of a ligand with respect to a protein molecule. The method is based on the iterative use of a convex, general quadratic approximation that underestimates a set of local minima, where the error in the approximation is minimized in the L¹ norm. The quadratic approximation is used to generate a reduced domain, which is assumed to contain the global minimum of the funnel shaped function. Additional local minima are computed in this reduced domain, and an improved approximation is computed. This process is iterated until a convergence tolerance is satisfied. The algorithm has been applied to find the global minimum of the energy function generated by the Docking Mesh Evaluator program. Results for three different protein docking examples are presented. Each of these energy functions has thousands of local minima. Convergence of the algorithm to an approximate global minimum is shown for all three examples.     

Application of the string method to compute minimum energy paths for a chain of bi-stable elements using the finite element method and molecular dynamics

Activated processes are frequently found in solid state mechanics. The energy landscape of such processes show a non-convex behaviour, and therefore the computation of energy barriers between two stable minima is of importance. Such barriers are revealed by computing minimum energy paths. The string method is a simple and efficient algorithm to move curves over an energy landscape and to identify minimum energy paths. A hierarchical two-scale model recently introduced to the literature (molecular dynamics coupled with the finite element method) is used in this paper to investigate the string method in a model phase transition in a copper single crystal. To do so, bi-stable elements are constructed and the energetic behaviour of a two-elements chain is investigated. We identify successfully the minimum energy path between two local stable minima of the chain and demonstrate thereby the performance of the string method applied to a complex multiscale model. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Oscillatory Fractional Brownian Motion

We introduce oscillatory analogues of fractional Brownian motion, sub-fractional Brownian motion and other related long range dependent Gaussian processes, we discuss their properties, and we show how they arise from particle systems with or without branching and with different types of initial conditions, where the individual particle motion is the so-called c-random walk on a hierarchical group. The oscillations are caused by the ultrametric structure of the hierarchical group, and they become slower as time tends to infinity and faster as time approaches zero. A randomness property of the initial condition increases the long range dependence. We emphasize the new phenomena that are caused by the ultrametric structure as compared with results for analogous models on Euclidean space.     

Least squares and Chebyshev fitting for parameter estimation in ODEs

We discuss the problem of determining parameters in mathematical models described by ordinary differential equations. This problem is normally treated by least squares fitting. Here some results from nonlinear mean square approximation theory are outlined which highlight the problems associated with nonuniqueness of global and local minima in this fitting procedure. Alternatively, for Chebyshev fitting and for the case of a single differential equation, we extend and apply the theory of [17, 18] which ensures a unique global best approximation. The theory is applied to two numerical examples which show how typical difficulties associated with mean square fitting can be avoided in Chebyshev fitting.This paper is presented as an outcome of the LMS Durham Symposium convened by Professor C.T.H. Baker on 4th-14th July 1992 with support from the SERC under Grant reference number GR/H03964.     

Global optimization by random perturbation of the gradient method with a fixed parameter

The paper deals with the global minimization of a differentiable cost function mapping a ball of a finite dimensional Euclidean space into an interval of real numbers. It is established that a suitable random perturbation of the gradient method with a fixed parameter generates a bounded minimizing sequence and leads to a global minimum: the perturbation avoids convergence to local minima. The stated results suggest an algorithm for the numerical approximation of global minima: experiments are performed for the problem of fitting a sum of exponentials to discrete data and to a nonlinear system involving about 5000 variables. The effect of the random perturbation is examined by comparison with the purely deterministic gradient method.     

Discrete approximation of symmetric jump processes on metric measure spaces

In this paper we give general criteria on tightness and weak convergence of discrete Markov chains to symmetric jump processes on metric measure spaces under mild conditions. As an application, we investigate discrete approximation for a large class of symmetric jump processes. We also discuss some application of our results to the scaling limit of random walk in random conductance.     

Explicit-implicit difference scheme for the joint solution of the radiative transfer and energy equations by the splitting method

High-order accurate explicit and implicit conservative predictor-corrector schemes are presented for the radiative transfer and energy equations in the multigroup kinetic approximation solved together by applying the splitting method with respect to physical processes and spatial variables. The original system of integrodifferential equations is split into two subsystems: one of partial differential equations without sources and one of ordinary differential equations (ODE) with sources. The general solution of the ODE system and the energy equation is written in quadratures based on total energy conservation in a cell. A feature of the schemes is that a new approximation is used for the numerical fluxes through the cell interfaces. The fluxes are found along characteristics with the interaction between radiation and matter taken into account. For smooth solutions, the schemes approximating the transfer equations on spatially uniform grids are second-order accurate in time and space. As an example, numerical results for Fleck’s test problems are presented that confirm the increased accuracy and efficiency of the method.     

Product-form characterization for a two-dimensional reflecting random walk

We consider a two-dimensional skip-free reflecting random walk on the non-negative integers, which is referred to as a 2-d reflecting random walk. We give necessary and sufficient conditions for the stationary distribution to have a product-form. We also derive simpler sufficient conditions for the product-form for a restricted class of 2-d reflecting random walks. We apply these results and obtain a product-form approximation of the stationary distribution through a suitable modification of the parameters of the random walk.     

Strong limit theorems for anisotropic random walks on ℤ2

We study the path behaviour of the anisotropic random walk on the two-dimensional lattice ?². Strong approximation of its components with independent Wiener processes is proved. We also give some asymptotic results for the local time in the periodic case.     

A scaling and energy equality preserving approximation for the 3D Navier-Stokes equations in the finite energy case

We discuss a new model (inspired by the work of Vishik and Fursikov) approximating the 3D Navier-Stokes equations, which preserves the scaling as in the Navier-Stokes equations and thus allows the study of self-similar solutions. Using some energy estimates and Leray’s limiting process, we show the existence of a solution of this model in the finite energy case, and the energy equality and inequality fulfilled by it. This approximation can be shown to converge to the Navier-Stokes equations using a mild approach based on the approximated pressure, and the solution satisfies Scheffer’s local energy inequality, an essential tool for proving Caffarelli, Kohn and Nirenberg’s regularity criterion. We also give a partial result of self-similarity satisfied by the approximated solution in the infinite energy case.     

Mathematical issues concerning the Boussinesq approximation for thermally coupled viscous flows

The flow of a viscous fluid driven by buoyancy forces is governed by balance equations for momentum, mass, and internal energy. Frequently, the Boussinesq approximation is employed to simplify the system, even in situations where dissipative heating cannot be neglected. The resulting equations violate the principle of conservation of total energy, which causes significant mathematical problems. We discuss these problems and possible remedies. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Growth of Nanophase Clusters and Potential Energy Minima: Hysteresis, Oscillations, and Phase Transitions

The structures of small Lennard-Jones clusters (local and global minima) in the range n = 30 - 55 atoms are investigated during growth by random atom deposition using Monte Carlo simulations. The cohesive energy, average coordination number, and bond angles are calculated at different temperatures and deposition rates. Deposition conditions which favor thermodynamically stable (global minima) and metastable (local minima) are determined. We have found that the transition from polyicosahedral to quasicrystalline structures during cluster growth exhibits hysteresis at low temperatures. A minimum critical size is required for the evolution of the quasicrystalline family, which is larger than the one predicted by thermodynamics and depends on the temperature and the deposition rate. Oscillations between polyicosahedral and quasicrystalline structures occur at high temperatures in a certain size regime. Implications for the applicability of global optimization techniques to cluster structure determination are also discussed.     

Microscopic justification of the stochastic f-model of critical dynamics

We use a microscopic approach to derive the stochastic Langevin equations for a quantum Bose liquid near the phase transition to the superfluid state. These equations can be obtained in local form if either the finiteness of the system dimensions or the boundedness of the time interval under study is taken into account. We give the microscopic expressions for random forces in the stochastic equations and show that the white noise approximation holds only in the critical region. We prove that the model for properly describing the critical fluctuations is precisely the F-model.     

Quenched tail estimate for the random walk in random scenery and in random layered conductance

We discuss the quenched tail estimates for the random walk in random scenery. The random walk is the symmetric nearest neighbor walk and the random scenery is assumed to be independent and identically distributed, non-negative, and has a power law tail. We identify the long time asymptotics of the upper deviation probability of the random walk in quenched random scenery, depending on the tail of scenery distribution and the amount of the deviation. The result is in turn applied to the tail estimates for a random walk in random conductance which has a layered structure.     

EQUIVALENT CONDITIONS OF LOCAL ASYMPTOTICS FOR THE OVERSHOOT OF A RANDOM WALK WITH HEAVY-TAILED INCREMENTS

This article gives the equivalent conditions of the local asymptotics for the overshoot of a random walk with heavy-tailed increments, from which we find that the above asymptotics are different from the local asymptotics for the supremum of the random walk. To do this, the article first extends and improves some existing results about the solutions of renewal equations.     

Modified method of splitting with respect to physical processes for solving radiation gas dynamics equations

An approach based on a modified splitting method is proposed for solving the radiation gas dynamics equations in the multigroup kinetic approximation. The idea of the approach is that the original system of equations is split using the thermal radiation transfer equation rather than the energy equation. As a result, analytical methods can be used to solve integrodifferential equations and problems can be computed in the multigroup kinetic approximation without iteration with respect to the collision integral or matrix inversion. Moreover, the approach can naturally be extended to multidimensional problems. A high-order accurate difference scheme is constructed using an approximate Godunov solver for the Riemann problem in two-temperature gas dynamics.