关于QR分解的注记

1 引言 在信号处理、时间序列分析中,QR分解有重要应用.设X_n={x_1~H…x_n~H}为n×p矩阵，每个x_i~H都是落入某个正则区间的数据样本。若序列{X_i~H}非平稳，则需要压缩老数据以抑制其污染新数据所带的信息。指数窗法是解决这个问题的一种重要而又常用     

A simulation model for emergency evacuation

This paper describes the development of a prototype spatial decision support system for use by emergency planners in developing contingency plans for evacuations from disaster areas. It links together a geographical information system (ARC/INFO) with a specially written object-oriented micro-simulator via a windowing computer operating system. The details of the system are described, its limitations are discussed and potential enhancements are identified.     

指标-2微分-代数系统的连续扩展Runge-Kutta迭代方法

1引言微分-代数系统包括具有约束条件的微分方程和奇异隐式微分方程,在实际应用中,如:约束力学系统、流体动力学、化学反应动力学、电子网络模拟、控制工程和机器人技术等领域就产生了诸多问题需要求解．近年来,微分-代数系统已极大地引起了许多工程师和数学工作者的关注,开展了众多相关问题的探讨,提出了许多新的算法理论[1-3]．在本文中我们对指标-2的微分-代数方程利用Runge-Kutta方法进行时间的离散和动力学迭代,研究离散迭代系统的收敛性．     

The New k-Windows Algorithm for Improving thek -Means Clustering Algorithm

The process of partitioning a large set of patterns into disjoint and homogeneous clusters is fundamental in knowledge acquisition. It is called Clustering in the literature and it is applied in various fields including data mining, statistical data analysis, compression and vector quantization. The k-means is a very popular algorithm and one of the best for implementing the clustering process. The k-means has a time complexity that is dominated by the product of the number of patterns, the number of clusters, and the number of iterations. Also, it often converges to a local minimum. In this paper, we present an improvement of the k-means clustering algorithm, aiming at a better time complexity and partitioning accuracy. Our approach reduces the number of patterns that need to be examined for similarity, in each iteration, using a windowing technique. The latter is based on well known spatial data structures, namely the range tree, that allows fast range searches.     

Windowed Green function method for wave scattering by periodic arrays of 2D obstacles

This paper introduces a novel boundary integral equation (BIE) method for the numerical solution of problems of planewave scattering by periodic line arrays of two-dimensional penetrable obstacles. Our approach is built upon a direct BIE formulation that leverages the simplicity of the free-space Green function but in turn entails evaluation of integrals over the unit-cell boundaries. Such integrals are here treated via the window Green function method. The windowing approximation together with a finite-rank operator correction—used to properly impose the Rayleigh radiation condition—yield a robust second-kind BIE that produces superalgebraically convergent solutions throughout the spectrum, including at the challenging Rayleigh–Wood anomalies. The corrected windowed BIE can be discretized by means of off-the-shelf Nyström and boundary element methods, and it leads to linear systems suitable for iterative linear algebra solvers as well as standard fast matrix–vector product algorithms. A variety of numerical examples demonstrate the accuracy and robustness of the proposed methodology.     

ARMA identification

In view of recent results on the asymptotic behavior of the prediction error covariance for a state variable system (see Ref. 1), an identification scheme for autoregressive moving average (ARMA) processes is proposed. The coefficients of thed-step predictor determine asymptotically the system momentsU ₀,...,U _d–1. These moments are also nonlinear functions of the coefficients of the successive 1-step predictors. Here, we estimate the state variable parameters by the following scheme. First, we use the Burg technique (see Ref. 2) to find the estimates of the coefficients of the successive 1-step predictors. Second, we compute the moments by substitution of the estimates provided by the Burg technique for the coefficients in the nonlinear functions relating the moments with the 1-step predictor coefficients. Finally, the Hankel matrix of moment estimates is used to determine the coefficients of the characteristic polynomial of the state transition matrix (see Refs. 3 and 4).A number of examples for the state variable systems corresponding to ARMA(2, 1) processes are given which show the efficiency of this technique when the zeros and poles are separated. Some of these examples are also studied with an alternative technique (see Ref. 5) which exploits the linear dependence between successive 1-step predictors and the coefficients of the transfer function numerator and denominator polynomials.In this paper, the problems of order determination are not considered; we assumed the order of the underlying system. We remark that the Burg algorithm is a robust statistical procedure. With the notable exception of Ref. 6 that uses canonical correlation methods, most identification procedures in control are based on a deterministic analysis and consequently are quite sensitive to errors. In general, spectral identification based on the windowing of data lacks the resolving power of the Burg technique, which is a super resolution method.This work was supported by NATO Research Grant No. 585/83, by University of Nice, by Thomson CSF-DTAS, by Instituto Nacional de Investigação Científica, and by CIRIT (Comissió Interdepartmental de Recerca i Innovació Technológica de Catalunya). The work of the third author was also partially supported by Army Research Office Contract DAAG-29-84-k-005.Simple ARMA(2, 1) Basic language analysis programs to construct random data were written by the second author and Dr. K. D. Senne, MIT Lincoln Laboratory. Lack of stability of the direct estimation was observed at TRW with the help of Dr. G. Butler. Analysis programs in FORTRAN for ARMA(p, q) were written and debugged at CAPS by the fifth author. The research was helped by access to VAXs at Thomson CSF-DTAS, Valbonne, France, at CAPS, Instituto Superior Técnico, and at Universitat Politécnica de Catalunya. In particular, the authors explicitly acknowledge Thomson CSF-DTAS and Dr. H. Gautier for extending the use of their facilities to the authors from September 1983 until June 1984, when the examples presented were simulated.on leave from University of Southern California, Los Angeles, California.formerly visiting at Massachusetts Institute of Technology and Laboratory for Information and Decision Systems, while on leave from Instituto Superior Técnico, Lisbon, Portugal.     

Minimum-Variance Multitaper Spectral Estimation on the Sphere

We develop a method to estimate the power spectrum of a stochastic process on the sphere from data of limited geographical coverage. Our approach can be interpreted either as estimating the global power spectrum of a stationary process when only a portion of the data are available for analysis, or estimating the power spectrum from local data under the assumption that the data are locally stationary in a specified region. Restricting a global function to a spatial subdomain—whether by necessity or by design—is a windowing operation, and an equation like a convolution in the spectral domain relates the expected value of the windowed power spectrum to the underlying global power spectrum and the known power spectrum of the localization window. The best windows for the purpose of localized spectral analysis have their energy concentrated in the region of interest while possessing the smallest effective bandwidth as possible. Solving an optimization problem in the sense of Slepian (1960) yields a family of orthogonal windows of diminishing spatiospectral localization, the best concentrated of which we propose to use to form a weighted multitaper spectrum estimate in the sense of Thomson (1982). Such an estimate is both more representative of the target region and reduces the estimation variance when compared to estimates formed by any single bandlimited window. We describe how the weights applied to the individual spectral estimates in forming the multitaper estimate can be chosen such that the variance of the estimate is minimized.     

The polyharmonic local sine transform: A new tool for local image analysis and synthesis without edge effect

We introduce a new local sine transform that can completely localize image information both in the space domain and in the spatial frequency domain. This transform, which we shall call the polyharmonic local sine transform (PHLST), first segments an image into local pieces using the characteristic functions, then decomposes each piece into two components: the polyharmonic component and the residual. The polyharmonic component is obtained by solving the elliptic boundary value problem associated with the so-called polyharmonic equation (e.g., Laplace's equation, biharmonic equation, etc.) given the boundary values (the pixel values along the boundary created by the characteristic function). Subsequently this component is subtracted from the original local piece to obtain the residual. Since the boundary values of the residual vanish, its Fourier sine series expansion has quickly decaying coefficients. Consequently, PHLST can distinguish intrinsic singularities in the data from the artificial discontinuities created by the local windowing. Combining this ability with the quickly decaying coefficients of the residuals, PHLST is also effective for image approximation, which we demonstrate using both synthetic and real images. In addition, we introduce the polyharmonic local Fourier transform (PHLFT) by replacing the Fourier sine series above by the complex Fourier series. With a slight sacrifice of the decay rate of the expansion coefficients, PHLFT allows one to compute local Fourier magnitudes and phases without revealing the edge effect (or Gibbs phenomenon), yet is invertible and useful for various filtering, analysis, and approximation purposes.     

The Fourier-series method for inverting transforms of probability distributions

This paper reviews the Fourier-series method for calculating cumulative distribution functions (cdf's) and probability mass functions (pmf's) by numerically inverting characteristic functions, Laplace transforms and generating functions. Some variants of the Fourier-series method are remarkably easy to use, requiring programs of less than fifty lines. The Fourier-series method can be interpreted as numerically integrating a standard inversion integral by means of the trapezoidal rule. The same formula is obtained by using the Fourier series of an associated periodic function constructed by aliasing; this explains the name of the method. This Fourier analysis applies to the inversion problem because the Fourier coefficients are just values of the transform. The mathematical centerpiece of the Fourier-series method is the Poisson summation formula, which identifies the discretization error associated with the trapezoidal rule and thus helps bound it. The greatest difficulty is approximately calculating the infinite series obtained from the inversion integral. Within this framework, lattice cdf's can be calculated from generating functions by finite sums without truncation. For other cdf's, an appropriate truncation of the infinite series can be determined from the transform based on estimates or bounds. For Laplace transforms, the numerical integration can be made to produce a nearly alternating series, so that the convergence can be accelerated by techniques such as Euler summation. Alternatively, the cdf can be perturbed slightly by convolution smoothing or windowing to produce a truncation error bound independent of the original cdf. Although error bounds can be determined, an effective approach is to use two different methods without elaborate error analysis. For this purpose, we also describe two methods for inverting Laplace transforms based on the Post-Widder inversion formula. The overall procedure is illustrated by several queueing examples.     

A unified set of proposals for control and design of high speed data networks

In this paper we articulate our philosophy and approach to the design and control of high speed data networks. The object is to put into perspective and to explain the coordination of various isolated pieces of detailed technical analyses that have been reported in several recent papers. In the process we summarize what we have learnt in our recent work and, also, we give indications of the direction of our future work. Our scheme integrates feedback and open loop control. The feedback control is exercised by sliding windows; access controllers regulate bursty sources. All our design proposals are rooted in asymptotic analyses; the justification for asymptotics comes from the largeness of the parameters, such as propagation delay, speed, window size, buffer size, and the number of virtual circuits. This analysis makes a strong case for operating in a specific moderate usage regime, and adaptive dynamic windowing algorithms are given that make this happen; moreover, when in this regime, buffers may be sized aggressively small without jeopardizing performance and the simplicity of the retransmission protocol. The topics in the paper are: model of communication, results on the steady-state behavior of the basic model, access control, small buffers and retransmission protocols, dynamic adaptive windows, bursty sources, and contrast with previous work.I. Mitrani's work was done while AT&T Bell Laboratories.     

Segment Intersection Searching Problems in General Settings

Abstract. We consider segment intersection searching amidst (possibly intersecting) algebraic arcs in the plane. We show how to preprocess n arcs in time O(n ^2+ɛ ) into a data structure of size O(n ^2+ɛ ) , for any ɛ >0 , such that the k arcs intersecting a query segment can be counted in time O( log n) or reported in time O( log n+k) . This problem was extensively studied in restricted settings (e.g., amidst segments, circles, or circular arcs), but no solution with comparable performance was previously presented for the general case of possibly intersecting algebraic arcs. Our data structure for the general case matches or improves (sometimes by an order of magnitude) the size of the best previously presented solutions for the special cases. As an immediate application of this result, we obtain an efficient data structure for the triangular windowing problem, which is a generalization of triangular range searching. As another application, the first substantially subquadratic algorithm for a red—blue intersection counting problem is derived. We also describe simple data structures for segment intersection searching among disjoint arcs, and for ray shooting among possibly intersecting arcs.     

Segment Intersection Searching Problems in General Settings

   Abstract. We consider segment intersection searching amidst (possibly intersecting) algebraic arcs in the plane. We show how to preprocess n arcs in time O(n ^2+ɛ ) into a data structure of size O(n ^2+ɛ ) , for any ɛ >0 , such that the k arcs intersecting a query segment can be counted in time O( log n) or reported in time O( log n+k) . This problem was extensively studied in restricted settings (e.g., amidst segments, circles, or circular arcs), but no solution with comparable performance was previously presented for the general case of possibly intersecting algebraic arcs. Our data structure for the general case matches or improves (sometimes by an order of magnitude) the size of the best previously presented solutions for the special cases. As an immediate application of this result, we obtain an efficient data structure for the triangular windowing problem, which is a generalization of triangular range searching. As another application, the first substantially subquadratic algorithm for a red—blue intersection counting problem is derived. We also describe simple data structures for segment intersection searching among disjoint arcs, and for ray shooting among possibly intersecting arcs.     

Error analysis of the combination technique

Summary. The combination technique is a method to reduce the computational time in the numerical approximation of partial differential equations. In this paper, we present a new technique to analyze the convergence rate of the combination technique. This technique is applied to general second order elliptic differential equations in two dimensions. Furthermore, it is proved that the combination technique for Poisson's equation convergences in arbitrary dimensions. Received September 25, 1997 / Revised version received October 22, 1998 / Published online September 7, 1999     

A new globalization technique for nonlinear conjugate gradient methods for nonconvex minimization

It is well-known that the HS method and the PRP method may not converge for nonconvex optimization even with exact line search. Some globalization techniques have been proposed, for instance, the PRP+ globalization technique and the Grippo-Lucidi globalization technique for the PRP method. In this paper, we propose a new efficient globalization technique for general nonlinear conjugate gradient methods for nonconvex minimization. This new technique utilizes the information of the previous search direction sufficiently. Under suitable conditions, we prove that the nonlinear conjugate gradient methods with this new technique are globally convergent for nonconvex minimization if the line search satisfies Wolfe conditions or Armijo condition. Extensive numerical experiments are reported to show the efficiency of the proposed technique.     

Probabilistic diversification and intensification in local search for vehicle routing

This article presents a probabilistic technique to diversify, intensify, and parallelize a local search adapted for solving vehicle routing problems. This technique may be applied to a very wide variety of vehicle routing problems and local searches. It is shown that efficient first-level tabu searches for vehicle routing problems may be significantly improved with this technique. Moreover, the solutions produced by this technique may often be improved by a postoptimization technique presented in this article, too. The solutions of nearly forty problem instances of the literature have been improved.     

A reliable aftertreatment for improving the differential transformation method and its application to nonlinear oscillators with fractional nonlinearities

A new aftertreatment technique is developed in this paper to deal with the truncated series derived by the differential transformation method (DTM) to obtain approximate periodic solutions. The proposed aftertreatment technique splits into two types, named as (Sine-AT technique, SAT) and (Cosine-AT technique, CAT). It is shown that the differential transformation method with the proposed aftertreatment technique is very effective and convenient for a class of nonlinear oscillatory problems with fractional nonlinearities without any need for Padé approximants or Laplace transform.     

基于有效Monte Carlo模拟的外汇期权组合非线性VaR模型

为了克服极小概率事件发生概率估计的困难,提出了把重要抽样技术发展到外汇期权组合非线性VaR模型中,估计出组合损失概率。为了进一步达到减少模拟估计误差目的,在重要抽样技术基础上使用分层抽样技术,进行更有效的Monte Carlo模拟。数值结果表明,重要抽样技术算法比常用Monte Carlo模拟法的计算效率更有效;而重要抽样技术和分层抽样技术相结合算法比重要抽样技术算法更有效地减少模拟所要估计的组合损失概率的方差,有着更高的计算效率。     

A useful technique for piecewise deterministic Markov decision processes

This note presents a technique that is useful for the study of piecewise deterministic Markov decision processes (PDMDPs) with general policies and unbounded transition intensities. This technique produces an auxiliary PDMDP from the original one. The auxiliary PDMDP possesses certain desired properties, which may not be possessed by the original PDMDP. We apply this technique to risk-sensitive PDMDPs with total cost criteria, and comment on its connection with the uniformization technique.     

Singular differential game numerical techniques

This paper develops a numerical technique to solve a class of zero-sum differential games with singular control. By using this technique and the application of inverse systems, a near-optimal closed-loop technique is developed to generate a numerical solution to this class of problems.     

6σ管理和统计技术

从6σ名称,绿带、黑带的评定标准和培训内容都体现了统计技术是6σ管理的核心技术。DMAIC统计技术应用模式虽有不足,最大功绩是使应用统计技术简单化,实际应用者认识了统计技术的重要性。