A temporal central limit theorem for real-valued cocycles over rotations

The rotor router model is a popular deterministic analogue of a random walk on a graph. Instead of moving to a random neighbor, the neighbors are served in a fixed order. We examine how fast this “deterministic random walk” covers all vertices (or all edges). We present general techniques to derive upper bounds for the vertex and edge cover time and derive matching lower bounds for several important graph classes. Depending on the topology, the deterministic random walk can be asymptotically faster, slower or equally fast compared to the classical random walk.

In this note, we present an example of a fibered dynamical system, also referred to as a deterministic random walk, generated by a piecewise linear expanding map on the first coordinate and a sign function on the second. We focus on the set of points whose orbits under this system remain non-negative in the second coordinate. Using the reflection principle from probability theory, we establish a connection between the topological pressure and a counting problem over constrained random walks. Furthermore, we show that the Hausdorff dimension of this set is invariant across horizontal sections and compute its Hausdorff measure.

Jim Propp's rotor–router model is a deterministic analog of a random walk on a graph. Instead of distributing chips randomly, each vertex serves its neighbors in a fixed order.Cooper and Spencer (Comb Probab Comput 15 (2006) 815–822) show a remarkable similarity of both models. If an (almost) arbitrary population of chips is placed on the vertices of a grid ℤd and does a simultaneous walk in the Propp model, then at all times and on each vertex, the number of chips on this vertex deviates from the expected number the random walk would have gotten there by at most a constant. This constant is independent of the starting configuration and the order in which each vertex serves its neighbors.This result raises the question if all graphs do have this property. With quite some effort, we are now able to answer this question negatively. For the graph being an infinite k‐ary tree (k ≥ 3), we show that for any deviation D there is an initial configuration of chips such that after running the Propp model for a certain time there is a vertex with at least D more chips than expected in the random walk model. However, to achieve a deviation of D it is necessary that at least exp(Ω(D2)) vertices contribute by being occupied by a number of chips not divisible by k at a certain time. © 2010 Wiley Periodicals, Inc. Random Struct. Alg., 2010

A cylindrical cascade on $\mathbb {T}^d\times \mathbb {R}^r$ can be seen as a deterministic random walk on $\mathbb {R}^r$ driven by an observable over the irrational toral translation on the base torus. We prove that, when the observable is the indicator function of a generic (straight) rectangle in $\mathbb {T}^2$ , the cascade on $\mathbb {T}^2\times \mathbb {R}$ is ergodic for a $G_{\delta }$ -dense set of translation vectors. We also provide examples of ergodic cylindrical cascades in higher dimensions with more restrictive conditions on the side lengths of the rectangles.

Jim Propp's rotor–router model is a deterministic analogue of a random walk on a graph. Instead of distributing chips randomly, each vertex serves its neighbours in a fixed order. We analyse the difference between the Propp machine and random walk on the infinite two-dimensional grid. It is known that, apart from a technicality, independent of the starting configuration, at each time the number of chips on each vertex in the Propp model deviates from the expected number of chips in the random walk model by at most a constant. We show that this constant is approximately 7.8 if all vertices serve their neighbours in clockwise or order, and 7.3 otherwise. This result in particular shows that the order in which the neighbours are served makes a difference. Our analysis also reveals a number of further unexpected properties of the two-dimensional Propp machine.

In this paper, the piecewise constant Block-Pulse functions and their operational matrices of integration have directly been used to solve a two-dimensional Fredholm-Volterra integral equation of second kind. This method presents a computational technique through converting this integral equation into a system of linear equations which can be easily solved by the known methods. Also the error analysis of this method will be considered. The efficiency and accuracy of the proposed method are illustrated by some examples.

In this paper we give a simple, explicit construction of polytopal bounded remainder sets of all possible volumes, for any irrational rotation on the $d$ dimensional adelic torus $\mathbb{A}^d/\mathbb{Q}^d$. Our construction involves ideas from dynamical systems and harmonic analysis on the adeles, as well as a geometric argument that reduces the existence argument to the case of an irrational rotation on the torus $\mathbb{R}^d/\mathbb{Q}^d$.

Let $T_{\rho}$ be an irrational rotation on a unit circle $S^{1}\simeq [0,1)$. Consider the sequence $\{\mathcal{P}_{n}\}$ of increasing partitions on $S^{1}$. Define the hitting times $N_{n}(\mathcal{P}_n;x,y):= \inf\{j\geq 1\mid T^{j}_{\rho}(y)\in P_{n}(x)\}$, where $P_{n}(x)$ is an element of $\mathcal{P}_{n}$ containing $x$. D. Kim and B. Seo in [9] proved that the rescaled hitting times $K_n(\mathcal{Q}_n;x,y):= \frac{\log N_n(\mathcal{Q}_n;x,y)}{n}$ a.e. (with respect to the Lebesgue measure) converge to $\log2$, where the sequence of partitions $\{\mathcal{Q}_n\}$ is associated with chaotic map $f_{2}(x):=2x \bmod 1$. The map $f_{2}(x)$ has positive entropy $\log2$. A natural question is what if the sequence of partitions $\{\mathcal{P}_n\}$ is associated with a map with zero entropy. In present work we study the behavior of $K_n(\tau_n;x,y)$ with the sequence of mixed partitions $\{\tau_{n}\}$ such that $ \mathcal{P}_{n}\cap [0,\frac{1}{2}]$ is associated with map $f_{2}$ and $\mathcal{D}_{n}\cap [\frac{1}{2},1]$ is associated with irrational rotation $T_{\rho}$. It is proved that $K_n(\tau_n;x,y)$ a.e. converges to a piecewise constant function with two values. Also, it is shown that there are some irrational rotations that exhibit different behavior.

where the empty sum is considered to be 0, N is a positive integer, and {Xnj :n = 0, 1, . . . ; j = 1, 2, . . . } and {φn(k):n, k = 0, 1, . . . } are independent sets of nonnegative-integer-valued random variables defined on the same probability space. The variables Xnj are independent and identically distributed with common probability law {pk}k≥0, pk := P(X01 = k), k = 0, 1, . . . , called the offspring probability distribution, and for n = 0, 1, . . . , {φn(k)}k≥0 are independent stochastic processes such that φn(k), n = 0, 1, . . . , are identically distributed. Let us denote by m := E[X01], σ := Var[X01], e(k) := E[φ0(k)], and ν(k) := Var[φ0(k)], k = 0, 1, . . . , the mean and variance (assumed finite) of the offspring probability distribution and the control random variables, respectively. From (1), it is easily verified that {Zn}n≥0 is a homogeneous Markov chain with state space on the nonnegative integers. For simplicity, we shall consider that the positive integers form a class of communicating and aperiodic states, and we will assume that P(φ0(0) = 0) = 1 and at least one of the following conditions holds: (i) p0 > 0; (ii) P(φ0(k) = 0) > 0, k = 1, 2, . . . . Under these assumptions, it was proved in [14] that the positive states are transient and, as a consequence, it is verified that P(Zn → 0) +P(Zn → ∞) = 1. (2)

The aim of this presentation is to construct a robust optimal PID feedback controller, taking into account stochastic uncertainties in the initial conditions. Usually, a precomputed feedback control is based on exactly known model parameters. However, in practice, often exact information about model parameters and initial values is not given. Hence, having an inital point, which differs from the nominal values, a standard precomputed controller may produce bad results. Supposing now that the probability distribution of the random parameter variations is known, in the following stochastic optimisation methods will be applied in order to obtain robust optimal feedback controls. Taking into account stochastic parameter variations at the initial point, the method works with expected total costs arising from the primary control expenses and the tracking error. Furthermore, the free regulator parameters are selected then such that the expected total costs are minimized. After Taylor expansion to calculate expected cost functions and a few transformations an approximate deterministic substitute control problem follows. Here, retaining only linear terms, approximation of expectations and variances of the expected cost functions can be calculated explicitly. By means of splines, numerical approximations of the objective function and the differential equations are obtained then. Using stochastic optimization methods, random parameter variations are incorporated into the optimal control process. Hence, robust optimal feedback controls are obtained. (© 2009 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

A decade of piecewise constant orthogonal functions in systems and control

Piecewise constant level set method for topology optimization of unilateral contact problems

PurposeA wide range of micro‐electro‐mechanical‐systems are based on the electrostatic principle, and for their design the computation of the electric capacities is of great importance. The purpose of this paper is to efficiently compute the capacities as a function of all possible positions of the two electrode structures within the transducer by an enhanced boundary element method (BEM).Design/methodology/approachA Galerkin BEM is developed and the arising algebraic system of equations is efficiently solved by a CG‐method with a multilevel preconditioner and an appropriate fast multipole algorithm for the matrix‐vector operations within the CG‐iterations.FindingsIt can be demonstrated that the piecewise linear and discontinuous trial functions give an approximation, which is almost as good as the one of the piecewise constant trial functions on the refined mesh, at lower computational costs and at about the same memory requirements.Originality/valueThe paper can proof mathematically and demonstrate in practice, that a higher order of convergence is achieved by using piecewise linear, globally discontinuous basis functions instead of piecewise constant basis functions. In addition, an appropriate preconditioner (artificial multilevel boundary element preconditioner, which is based on the Bramble Pasciak Xu like preconditioner) has been developed for the fast iterative solution of the algebraic system of equations.

ABSTRACTThis paper gives two methods for the L1 analysis of sampled-data systems, by which we mean computing the L∞-induced norm of sampled-data systems. This is achieved by developing what we call the kernel approximation approach in the setting of sampled-data systems. We first consider the lifting treatment of sampled-data systems and give an operator theoretic representation of their input/output relation. We further apply the fast-lifting technique by which the sampling interval [0, h) is divided into M subintervals with an equal width, and provide methods for computing the L∞-induced norm. In contrast to a similar approach developed earlier called the input approximation approach, we use an idea of kernel approximation, in which the kernel function of an input operator and the hold function of an output operator are approximated by piecewise constant or piecewise linear functions. Furthermore, it is shown that the approximation errors in the piecewise constant approximation or piecewise linear approximation scheme converge to 0 at the rate of 1/M or 1/M2, respectively. In comparison with the existing input approximation approach, in which the input function (rather than the kernel function) of the input operator is approximated by piecewise constant or piecewise linear functions, we show that the kernel approximation approach gives improved computation results. More precisely, even though the convergence rates in the kernel approximation approach remain qualitatively the same as those in the input approximation approach, the newly developed former approach could lead to quantitatively improved approximation errors than the latter approach particularly when the piecewise linear approximation scheme is taken. Finally, a numerical example is given to demonstrate the effectiveness of the kernel approximation approach with this scheme.

Review of Five Sets of Piecewise Constant Orthogonal Functions for Function Approximation, Integration and Solution of First Order Differential Equation Using These Function Sets