散乱数据点多项式插值光顺曲面的构造

为了得到光顺的多项式插值曲面,首先把空间散乱数据点划分为三角形网格,在每个给定数据点处构造C^1连续的分片二次多项式曲面片,针对各数据点的邻接点个数不同,分别利用弯折能量和拉伸能量建立目标函数,极小化目标函数确定插值曲面的未知量,在保持原有的形状特征的同时构造光顺的分片插值曲面,最后用实例说明了文中方法的有效性．     

Semi-tensor product of matrices and its application to Morgen's problem

This paper proposes a new matrix product, namely, semi-tensor product. It is a general-ization of the conventional matrix product. Meanwhile, it is also closely related to Kronecker (tensor) product of matrices. The purpose of introducing this product is twofold: (i) treat multi-dimensional da-ta; (ii) treat nonlinear problems in a linear way. Then the computer and numerical methods can be easily used for solving nonlinear problems. Properties and formulas are deduced. As an application, the Morgan's problem for control systems is formulated as a numerically solvable problem.     

A new algebra of Toeplitz-plus-Hankel matrices and applications

We introduce a new simultaneously diagonalizable real algebra of symmetrical centrosymmetrical matrices having a Toeplitz-plus-Hankel structure. We give the corresponding orthonormal basis of eigenvectors which are alternately symmetrical and skewsymmetrical vectors. An application is the construction of a symmetrical Toeplitz-plus-centrosymmetrical Hankel matrix of equal row sums having a prescribed real spectrum. This matrix can be used as the starting matrix for symmetrical centrosymmetrical isospectral flows. In particular, for the isospectral flow corresponding to the construction of a regular Toeplitz matrix having prescribed eigenvalues. Moreover, if A is a noise representation of an unknown matrix in of rank k then we give a procedure to approximate A by a matrix in of rank k.     

Operational matrices of Bernstein polynomials and their applications

The Bernstein polynomials (B-polynomials) operational matrices of integration P, differentiation D and product ? are derived. A general procedure of forming these matrices are given. These matrices can be used to solve problems such as calculus of variations, differential equations, optimal control and integral equations. Illustrative examples are included to demonstrate the validity and applicability of the operational matrices.     

A new approach to the epsilon expansion of generalized hypergeometric functions

Assuming that the parameters of a generalized hypergeometric function depend linearly on a small variable ε$\epsilon$, the successive derivatives of the function with respect to that small variable are evaluated at ε=0$\epsilon =0$ to obtain the coefficients of the ε$\epsilon$-expansion of the function. The procedure, which is quite naive, benefits from simple explicit expressions of the derivatives, to any order, of the Pochhammer and reciprocal Pochhammer symbols with respect to their argument. The algorithm may be used algebraically, irrespective of the values of the parameters. It reproduces the exact results obtained by other authors in cases of especially simple parameters. Implemented numerically, the procedure improves considerably, for higher orders in ε$\epsilon$, the numerical expansions given by other methods.     

An approximation method for eigenvectors of very large matrices

A Monte-Carlo approach for solving huge, dense matrices for eigenvalues and eigenvectors is proposed. The matrix must satisfy certain conditions including a smooth density of diagonal elements curve and relatively constant off-diagonal elements. The approach simply involves randomly choosing a finite order (as large as computationally possible) subset matrix from the original matrix and then diagonalizing the subset. The results are crude, but often informative.     

Multilevel preconditioning of graph-Laplacians: Polynomial approximation of the pivot blocks inverses

We consider the discrete system resulting from mixed finite element approximation of a second-order elliptic boundary value problem with Crouzeix–Raviart non-conforming elements for the vector valued unknown function and piece-wise constants for the scalar valued unknown function. Since the mass matrix corresponding to the vector valued variables is diagonal, these unknowns can be eliminated exactly. Thus, the problem of designing an efficient algorithm for the solution of the resulting algebraic system is reduced to one of constructing an efficient algorithm for a system whose matrix is a graph-Laplacian (or weighted graph-Laplacian).     

Parikh matrices and amiable words

Using the fact that the Parikh matrix mapping is not an injective mapping, the paper investigates some properties of the set of words with the same Parikh matrix; these words are called “amiable”. The presented results extend the results obtained in [A. Atanasiu, Binary amiable words, Int. J. Found. Comput. Sci. 18 (2) (2007) 387–400] for the binary case. In particular it is shown that all the words having the same Parikh matrix can be obtained one from another by applying only two types of transformations. Moreover, the mirrors of two amiable words are also amiable (thus forming a symmetrical class of words).     

粗糙集上下近似的矩阵刻画及应用

主要对粗糙集中上下近似的矩阵刻画及应用进行了研究。给出等价关系、一般二元关系、基于邻域的覆盖粗糙集下一种上下近似的新的矩阵刻画;作为应用,提出关系矩阵方程,并对上下近似的逆问题进行了研究,即在已知关系矩阵[MR,]上（下）近似[R(X)][(R(X))]的情况下反解[X,]给出了求解[X]的方法。     

An approximation method for high-order fractional derivatives of algebraically singular functions

The fractional derivative D^qf(s) (0≤s≤1) of a given function f(s) with a positive non-integer q is defined in terms of an indefinite integral. We propose a uniform approximation scheme to D^qf(s) for algebraically singular functions f(s)=s^αg(s) (α>−1) with smooth functions g(s). The present method consists of interpolating g(s) at sample points t_j in [0,1] by a finite sum of the Chebyshev polynomials. We demonstrate that for the non-negative integer m such that m<q<m+1, the use of high-order derivatives g⁽ⁱ⁾(0) and g⁽ⁱ⁾(1) (0≤i≤m) at both ends of [0,1] as well as g(t_j), t_j∈[0,1] in interpolating g(s), is essential to uniformly approximate D^q{s^αg(s)} for 0≤s≤1 when α≥q−m−1. Some numerical examples in the simplest case 1<q<2 are included.     

Optimal bounds for the approximation of boolean functions and some applications

This paper presents an optimal bound on the Shannon function L(n,m,) that gives the worstcase circuit-size complexity to approximate, within an approximation degree at least , partial boolean functions having n inputs and domain size m. That is

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. Our bound applies to any partial boolean function and any approximation degree, and thus completes the study of boolean function approximation introduced by Pippenger (1977).

Our results give an upper bound for the hardness function h(ƒ), introduced by Nisan and Wigderson (1994), which denotes the minimum value l for which there exists a circuit of size at most l that approximates a boolean function ƒ with degree at least 1/l. Indeed, if H(n) denotes the maximum hardness value achieved by boolean functions with n inputs, we prove that for almost every nH(n)n/3 + n² + O(1). The exponent n/3 in the above inequality implies that no family of boolean functions exists which has ‘full’ hardness. This fact establishes connections with Allender and Strauss' (1994) work that explores the structure of BPP.

Finally, we show that for almost every n and for almost every boolean function ƒ of n inputs we have h(ƒ)2^{n/3−2 log n}. The contribution in the proof of the upper bound for L(n, m, ) can be viewed as a set of technical results that globally show how boolean linear operators are ‘well’ distributed on the class of 4-regular domains. This property is then applied to approximate partial boolean functions on general domains using a suitable composition of boolean linear operators.     

Block TERM factorization of block matrices

Duetothelimitationofcomputationalprecisionandstoragecapacity,transformsusedinlosslessdatacompressionshouldbeequivalentlyinteger-reversible.Reversibleintegertransform(orintegermapping)issuchatypeoftransformthatmapsintegerstointegersandrealizesperfectreconstruction(PR).Peoplestartedtoworkinthisarealongago,andtheirearlywork,suchasStransform[1],TStransform[2],S+Ptransform[3],andcolorspacetransforms[4],suggestedapromisingfutureofreversibleintegermappinginimagecompression,region-of-interest(ROI)…     

Neural network approximation for periodically disturbed functions and applications to control design

This paper addresses the approximation problem of functions affected by unknown periodically time-varying disturbances. By combining Fourier series expansion into multilayer neural network or radial basis function neural network, we successfully construct two kinds of novel approximators, and prove that over a compact set, the new approximators can approximate a continuously and periodically disturbed function to arbitrary accuracy. Then, we apply the proposed approximators to disturbance rejection in the first-order nonlinear control systems with periodically time-varying disturbances, but it is straightforward to extend the proposed design methods to higher-order systems by using adaptive backstepping technique. A simulation example is provided to illustrate the effectiveness of control schemes designed in this paper.     

Interpolation Formulas of the Lagrange Type in a Hilbert Space that are Invariant with Respect to Polynomials

In a Hilbert space, interpolation formulas of the Lagrange type that retain degrees of interpolated polynomials are constructed. The case of a Euclidean space is considered.     

Chebyshev approximation of functions by the sum of a polynomial and an expression with a nonlinear parameter and endpoint interpolation

Sufficient existence conditions are established for the uniform Chebyshev (minimax) approximation of a function by the sum of a polynomial and an expression with a nonlinear parameter with the minimum absolute error and interpolation at the interval endpoints. An algorithm for determining the parameters of such an approximation using the Remez algorithm is proposed. The application of the iterative method to calculating the nonlinear parameter is substantiated. Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 64–75, January–February 2009.     

同心圆与直线剖分下的多元多项式插值

传统的插值方法一般是基于三角形或四边形剖分的，在应用上不易处理类似于呈圆形分布的问题，有一定的局限性．给出一种新的基于同心圆与直线剖分的插值方法，由于该剖分的节点分布是对称的，加之所构造的基函数是对称的，因而插值函数具有保对称性，且是多项式函数．数值实例表明，该插值方法对此类问题有很好的效果，并给出了相应的误差分析．另外，若剖分线退化为射线，该方法可适用更一般情形．     

Polynomial expansions of boolean functions in images of nonhomogeneous operators

To represent the functions in terms of which expansions are constructed, the so-called dp-, pt -, and dt -operators are used. The existence of polynomial expansions of Boolean functions in functions obtained by applying heterogeneous operators of the above-mentioned types to a nondegenerate Boolean function is proved. Some methods of finding coefficients of these expansions are also considered. Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 40–55, May–June, 2000.     

颜色共生矩阵的Fisher信息度量及识别应用

基于多项流形的黎曼几何,提出一个在矩阵流形框架下度量颜色共生矩阵信息差异并将其应用于目标识别的新方法。对于给定的颜色量化水平和每个像素局部邻域,该方法将一幅彩色图像的任意两个颜色通道中共生的颜色建模为一个潜在的多项分布的概率实现。通过基于紧化的共生频率嵌入,可将每幅图像等同为一个积矩阵流形上的一点,其中每个因子流形被赋予了从对应的多项流形上诱导的Fisher信息距离度量。对于一个识别任务,测试样本与训练样本间的匹配通过先在每个因子流形上使用最近邻分类器进行标签预测然后在积流形上进行多数投票完成。在GT彩色人脸库和COIL-100目标库上获得的出色的识别效果验证了该方法的有效性。