Boundedness of adjoint bases of approximate spectral subspaces and of associated block reduced Resolvents

Block reduced resolvents are often employed in iterative schemes for refining crude approximations of the arithmetic mean of a cluster of eigenvalues and of a basis of the corresponding spectral subspace. We prove that if the bases of approximate spectral subspaces are chosen in such a way that they are bounded and each element of the basis is bounded away from the span of the previously chosen elements, then the corresponding adjoint bases are also bounded. We give an integral representation of the associated block reduced resolvent and show that under such a choice of the bases, the approximate block reduced resolvents are bounded as well. This is crucial in obtaining error estimates for the iterates of several refinement schemes. In the framework of a canonical discretization procedure for finite rank operators, appropriate choices of ises are given for various finite rank approximation methods such as Projection, Sloan, Galerkin, Nyström, Fredholm, Degenerate kernel. If the bases are not chosen appropriately, the error estimates may no longer hold and the iteration scheme may not be numerically stable. Examples are given to illustrate these phenomena     

Kuramoto-Sivashinsky方程的B样条Galerkin方法

采用显隐结合的方法对微分算子进行时间离散 ,提出了解 Kuramoto-Sivashinsky方程的全离散 B样条 Galerkin方法 ,由此得到了有限元解的最优阶收敛性及稳定性估计 .最后的数值算例以图形的形式体现了此算法的精确度     

Adaptive compensation of uncertain Euler–Lagrange systems with multiple time-varying actuator faults

This work proposes the command tracking problem for uncertain Euler–Lagrange (EL) systems with multiple partial loss of effectiveness (PLOE) actuator faults. Compared to existing fault-tolerant controllers for EL systems, the proposed adaptive controller accounts for parametric uncertainties in the system and multiple time-varying actuator fault parameters. The proposed method can also handle an infinite number of fault cases. The closed-loop fault-tolerant system is treated as a switched dynamical system, and a switched system stability is established using multiple Lyapunov functions. It is shown that all signals are bounded in each sub-interval and at the switching instances, and asymptotic tracking can be obtained only for a finite number of fault occurrences, whereas tracking error is bounded for the infinite case. Finally, a simulation example on a robotic manipulator is presented to show the effectiveness of the proposed method.     

Analysis of the parareal approach based on discontinuous Galerkin method for time-dependent Stokes equations

This paper analyzes a parareal approach based on discontinuous Galerkin (DG) method for the time-dependent Stokes equations. A class of primal discontinuous Galerkin methods, namely variations of interior penalty methods, are adopted for the spatial discretization in the parareal algorithm (we call it parareal DG algorithm). We study three discontinuous Galerkin methods for the time-dependent Stokes equations, and the optimal continuous in time error estimates for the velocities and pressure are derived. Based on these error estimates, the proposed parareal DG algorithm is proved to be unconditionally stable and bounded by the error of discontinuous Galerkin discretization after a finite number of iterations. Finally, some numerical experiments are conducted which confirm our theoretical results, meanwhile, the efficiency of the parareal DG algorithm can be seen through a parallel experiment.     

一类死区非线性输入系统的自适应模糊控制

针对一类具有死区非线性输入的非线性系统,基于滑模控制的基本原理,利用II型模糊逻辑系统对未知函数进行在线逼近,提出了一种具有监督器的自适应模糊滑模控制方法。该方法通过监督控制器保证闭环系统所有信号有界,并通过引入最优逼近误差的自适应补偿项来消除建模误差的影响。通过理论分析,证明了跟踪误差收敛到零。     

Computing the Expected Markov Reward Rates with Stationarity Detection and Relative Error Control

By combining in a novel way the randomization method with the stationary detection technique, we develop two new algorithms for the computation of the expected reward rates of finite, irreducible Markov reward models, with control of the relative error. The first algorithm computes the expected transient reward rate and the second one computes the expected averaged reward rate. The algorithms are numerically stable. Further, it is argued that, from the point of view of run-time computational cost, for medium-sized and large Markov reward models, we can expect the algorithms to be better than the only variant of the randomization method that allows to control the relative error and better than the approach that consists in employing iteratively the currently existing algorithms that use the randomization method with stationarity detection but allow to control the absolute error. The performance of the new algorithms is illustrated by means of examples, showing that the algorithms can be not only faster but also more efficient than the alternatives in terms of run-time computational cost in relation to accuracy.     

Galerkin's method and stability

Approximation in least squares by Galerkin's method leads to a consideration of strongly minimal systems. Theorems are derived which permit the recognition of systems which are not strongly minimal from the characteristics of the elements themselves. Normalised systems cannot be strongly minimal without their eigenvalues being bounded above. Speared systems, which have desirable properties, are introduced and their main features determined. Convergence earmarks and error bounds are exposed. A new definition of stability, which is self-checking in a computation, is suggested and its attributes delineated. The extension of the theory to equations involving positive-definite operators is mentioned.     

Numerically stable,scalable formulas for parallel and online computation of higher-order multivariate central moments with arbitrary weights

Formulas for incremental or parallel computation of second order central moments have long been known, and recent extensions of these formulas to univariate and multivariate moments of arbitrary order have been developed. Such formulas are of key importance in scenarios where incremental results are required and in parallel and distributed systems where communication costs are high. We survey these recent results, and improve them with arbitrary-order, numerically stable one-pass formulas which we further extend with weighted and compound variants. We also develop a generalized correction factor for standard two-pass algorithms that enables the maintenance of accuracy over nearly the full representable range of the input, avoiding the need for extended-precision arithmetic. We then empirically examine algorithm correctness for pairwise update formulas up to order four as well as condition number and relative error bounds for eight different central moment formulas, each up to degree six, to address the trade-offs between numerical accuracy and speed of the various algorithms. Finally, we demonstrate the use of the most elaborate among the above mentioned formulas, with the utilization of the compound moments for a practical large-scale scientific application.     

Macroscopic modeling and simulations of room evacuation

We analyze numerically two macroscopic models of crowd dynamics: the classical Hughes model and the second order model being an extension to pedestrian motion of the Payne–Whitham vehicular traffic model. The desired direction of motion is determined by solving an eikonal equation with density dependent running cost, which results in minimization of the travel time and avoidance of congested areas. We apply a mixed finite volume-finite element method to solve the problems and present error analysis for the eikonal solver, gradient computation and the second order model yielding a first order convergence. We show that Hughes’ model is incapable of reproducing complex crowd dynamics such as stop-and-go waves and clogging at bottlenecks. Finally, using the second order model, we study numerically the evacuation of pedestrians from a room through a narrow exit.     

曲线的圆弧逼近与双圆弧逼近

众所周知,圆弧具有一系列简单而又重要的性质(如直观、实现方便、具有几何不变性、便于计算机存贮和传递图形等).目前国内外绝大多数插补器都采用直线-圆弧插补,因而研究曲线的圆弧逼近与拟合对于计算机辅助设计和制造有重要的现实意义.以前的计算方法很少研究象圆弧一类的几何曲线,近十几年由于计算机和数控技术的发展和应     

Fast and Flexible ADMM Algorithms for Trend Filtering

This article presents a fast and robust algorithm for trend filtering, a recently developed nonparametric regression tool. It has been shown that, for estimating functions whose derivatives are of bounded variation, trend filtering achieves the minimax optimal error rate, while other popular methods like smoothing splines and kernels do not. Standing in the way of a more widespread practical adoption, however, is a lack of scalable and numerically stable algorithms for fitting trend filtering estimates. This article presents a highly efficient, specialized alternating direction method of multipliers (ADMM) routine for trend filtering. Our algorithm is competitive with the specialized interior point methods that are currently in use, and yet is far more numerically robust. Furthermore, the proposed ADMM implementation is very simple, and, importantly, it is flexible enough to extend to many interesting related problems, such as sparse trend filtering and isotonic trend filtering. Software for our method is freely available, in both the C and R languages.     

Numerical analysis of a method for high Peclet number transport in porous media

A variationally consistent eddy viscosity discretization is presented in [W.J. Layton, A connection between subgrid scale eddy viscosity and mixed methods, Appl. Math. Comput. 133 (2002) 147-157] for the stationary convection diffusion problem. This discretization is extended to the evolutionary problem in [N. Heitmann, Subgridscale stabilization of time-dependent convection dominated diffusive transport, J. Math. Anal. Appl. 331 (2007) 38-50] with a near optimal error bound. In the following, we couple this discretization with the porous media problem. We present a comprehensive analysis of stability and error for the velocity field derived from the porous media problem. Next, using a backward Euler approximation for the time derivative we follow the inherited error in velocity through the coupling with the convection diffusion problem. The method is shown to be stable and the error near optimal and independent of the diffusion coefficient, ?.     

Coupled-Mode Equations and Gap Solitons in a Two-Dimensional Nonlinear Elliptic Problem with a Separable Periodic Potential

We address a two-dimensional nonlinear elliptic problem with a finite-amplitude periodic potential. For a class of separable symmetric potentials, we study the bifurcation of the first band gap in the spectrum of the linear Schrödinger operator and the relevant coupled-mode equations to describe this bifurcation. The coupled-mode equations are derived by the rigorous analysis based on the Fourier–Bloch decomposition and the implicit function theorem in the space of bounded continuous functions vanishing at infinity. Persistence of reversible localized solutions, called gap solitons, beyond the coupled-mode equations is proved under a nondegeneracy assumption on the kernel of the linearization operator. Various branches of reversible localized solutions are classified numerically in the framework of the coupled-mode equations and convergence of the approximation error is verified. Error estimates on the time-dependent solutions of the Gross–Pitaevskii equation approximated by solutions of the coupled-mode equations are obtained for a finite-time interval.     

Quasi-interpolation by thin-plate splines on a square

Quasi-interpolation is one method of generating approximations from a space of translates of dilates of a single function ψ. This method has been applied widely to approximation by radial basis functions. However, such analysis has most often been performed in the setting of an infinite uniform grid of centers. In this paper we develop general error bounds for approximation by quasiinterpolation on ann-cube. The quasi-interpolant analyzed involves a finite number, growing ash ^?n , of translates of dilates of the function ψ, and a bounded number of edge functions. The centers of the translates of dilates of ψ form a uniformly spaced grid within the cube. These error bounds are then applied to approximation by thin-plate splines on a square. The result is an O(ω(f, [-1,1]²,h)) error bound for approximation by thin-plate splines supplemented with eight arctan functions.     

Accuracy and conservation properties in numerical integration: the case of the Korteweg-de Vries equation

Summary. When numerically integrating time-dependent differential equations, it is often recommended to employ methods that preserve some of the invariant quantities (mass, energy, etc.) of the problem being considered. This recommendation is usually justified on the grounds that conservation of invariant quantities may ensure that the numerical solution possesses some important qualitative features. However there are cases where schemes that preserve invariants are also advantageous in that they possess favourable error propagation mechanisms that render them superior from a quantitative point of view. In the present paper we consider the Korteweg-de Vries equation as a case study. We show rigorously that, for soliton problems and at leading order, the error of conservative schemes consists of a phase error that grows linearly with time plus a complementary term that is bounded in the norm uniformly in time. For ‘general’, nonconservative schemes the error involves a linearly growing amplitude error, a quadratically growing phase error and a complementary term that grows linearly in the norm. Numerical experiments are presented. Received November 21, 1994 / Revised version received July 17, 1995     

Discretization of time-dependent quantum systems: real-time propagation of the evolution operator

We discuss time-dependent quantum systems on bounded domains. Our work may be viewed as a framework for several models, including linear iterations involved in time-dependent density functional theory, the Hartree-Fock model, or other quantum models. A key aspect of the analysis of the algorithms is the use of time-ordered evolution operators, which allow for both a well-posed problem and its approximation. The approximation theorems obtained for the time-ordered evolution operators complement those in the current literature. We discuss the available theory at the outset, and proceed to apply the theory systematically in later sections via approximations and a global existence theorem for a nonlinear system, obtained via a fixed point theorem for the evolution operator. Our work is consistent with first-principle real-time propagation of electronic states, aimed at finding the electronic responses of quantum molecular systems and nanostructures. We present two full 3D quantum atomistic simulations using the finite element method for discretizing the real space, and the FEAST eigenvalue algorithm for solving the evolution operator at each time step. These numerical experiments are representative of the theoretical results.     

基于观测器的直接自适应模糊控制

针对一类不确定非线性系统,利用H∞控制技术和T-S模糊系统,提出了一种基于观测器的直接自适应模糊控制方法。该方法不需要系统的状态变量完全可测。通过引入最优逼近误差补偿项,取消了最优逼近误差平方可积的假设条件。基于Lyapunov稳定理论,证明了闭环自适应模糊系统是半全局一致终结有界的,且跟踪误差渐近收敛到零。仿真结果表明所提设计方法的有效性。     

Model reduction by iterative error system approximation

The analysis of a posteriori error estimates used in reduced basis methods leads to a model reduction scheme for linear time-invariant systems involving the iterative approximation of the associated error systems. The scheme can be used to improve reduced-order models (ROMs) with initial poor approximation quality at a computational cost proportional to that for computing the original ROM. We also show that the iterative approximation scheme is applicable to parametric systems and demonstrate its performance using illustrative examples.     

关于两类具有不同基函数的标准模糊系统逼近精度问题的研究

在标准模糊系统的基础上提出了以正规二次多项式和正规三角函数为基函数的两类标准模糊系统.通过采用数值分析中的余项与辅助函数方法,对这两类模糊系统进行了误差精度的分析,给出了从SISO到MISO的误差界公式.同时,对这两类模糊系统误差界进行了比较,指出了两类模糊系统的优劣.最后,通过算例验证了理论结果的正确性.     

Discrete Approximations of Controlled Stochastic Systems with Memory: A Survey

This survey article considers discrete approximations of an optimal control problem in which the controlled state equation is described by a general class of stochastic functional differential equations with a bounded memory. Specifically, three different approximation methods, namely (i) semidiscretization scheme; (ii) Markov chain approximation; and (iii) finite difference approximation, are investigated. The convergence results as well as error estimates are established for each of the approximation methods.