线性流形上对称正交对称矩阵逆特征值问题

1.引言 令R~(n×m)表示所有n×m阶实矩阵集合;OR~(n×n)表示所有n阶正交矩阵全体;A~+表示A的Moore-penrose广义逆;I_к表示К阶单位阵;SR~(n×n)表示n阶实对称矩阵的全体;rank(A)表示A的秩;||·||是矩阵的Frobenius范数;对A=(a_(ij)),B=(b_(ij))∈R~(n×m),A＊B表示A与B的Hadamard乘积,其定义为A*B=(a_(ij),b_(ij))。     

对称次反对称矩阵的一类反问题

1 引言 用R~(m×n),SR~(n×n),ASR~(n×n),OR~(n×n)分别表示所有m×n实矩阵,n阶实对称矩阵,n阶实反对称矩阵和n阶实正交矩阵组成的集合,I_k表示k阶单位矩阵,S_k表示k阶反序单位矩阵,||A||表示矩阵A的Frobenius范数。若A=(a_(ij))∈R~(n×n),记D_A=diag(a_(11),a_(22),…,a_(nn)),L_A=(l_(ij))∈R_(n×n)其中当i>j时,l_(ij)=a_(ij),当i≤j时,l_(ij)=0,(i,j=1,2,…,n).若A=(a_(ij)),B=(b_(ij))∈R~(m×n),A*B表示A与B的Hadamard乘积,其定义为A*B=(a_(ij)b_(ij))。     

线性流形上实对称矩阵最佳逼近

1.引言 首先介绍一些记号,IR~(n×m)表示所有n×m实矩阵的全体,SIR~(n×n)表示所有n×n实对称矩阵的全体,OIR~(n×n)表示所有n×n正交矩阵的全体,I_n表示n阶单位矩阵,A~T和A~+分别表示矩阵A的转置和Moore-Penrose广义逆。对A=(a_(ij)),B=(b_(ij))∈IR~(n×m),A＊B表示A与B的Hadamard积,定义为A＊B=(a_(ij)b_(ij)),并且定义A与B的内积     

一类对称正交对称矩阵反问题的最小二乘解

1 引言 本文记号R~(n×m),OR~(n×n),A~+,I_k,SR~(n×n),rank(A),||·||,A*B,BSR~(n×n)和ASR~(n×n)参见[1].若无特殊声明文中的P为一给定的矩阵且满足P∈OR~(n×n)和P=P~T. 定义1 设A=(α_(ij))∈R~(n×n).若A满足A=A~T,(PA)~T=PA则称A为n阶对称正交对称矩阵;所有n阶对称正交对称矩阵的全体记为SR_P~n.若A∈R~(n×n)满足A~T=A,(PA)~T=-PA,则称A为n阶对称正交反对称矩阵;所有n阶对称正交反对     

SOME INEQUALITIES OF GENERALIZED SINGULAR VALUES OF MATRICES

@1 Definition 1 Let A=(α_(ij))∈C~(n×n),B=(b_(ij))∈C~(n×n),is nonsingular.The generalizedsingular values of A(relative to B)are following determinate nonnegative real numberswhen ||·||_2 denotes the Euclid vector norm,〈n〉={1,2,…,n}.Definition 2 Let A,B∈C~(n×n),if there exist λ∈C and x∈C~n\{0},such     

广义严格对角占优矩阵的迭代判别方法

正1引言设A=(a_(ij))∈C~(n×n),N={1,2,…,n}.记R_i(A)= sum |a_(ij)| from j≠i (i∈N),又记N_1=N_1(A)={i∈N:0|a_(ii)|≤R_i(A)},N_2=N_2(A)={i∈N:|a_(ii)R_i(A)}.定义1设A=(a_(ij))∈C~(n×n),如果|a_(ii)|R_i(A)(i∈N),则称A为严格对角占优矩阵.严格对角占优矩阵的集合记为D.如果存在n阶正对角矩阵D使得AD∈D,则称A为广义严格对角占优矩阵.广义严格对角占优矩阵的集合记为D.     

子矩阵约束下矩阵方程AX=B的正交投影迭代解法

<正>1引言记R~(m×n)为全体m×n阶实矩阵集合;给定矩阵A,B∈R~(m×n),记(A,B)=tr(A~TB)为矩阵A与B的内积;||A||_F=(A,A)~(1/2)=(tr(A~TA))~(1/2)为矩阵A的Frobenius范数;vec(A)为矩阵A的拉直向量;A(p_1:p_2,)为矩阵A的pz行到p2行元素组成的子矩阵;A(,q_1:q_2)为矩阵A的q_1列到q_2列元素组成的子矩阵;A(p_1:p_2,q_1:q_2)为矩阵A的p_1行到p_2行和q_1列到q_2列相交处元素组成的子矩阵;如果(A,B)=tr(A~TB)=0,则称     

广义对角占优矩阵与M—矩阵的判定准则

广义对角占优矩阵与M—矩阵是计算数学中应用极其广泛的矩阵类。作者在文[1]中证明若A=(α_(ij))∈C~(n×n)为具有非零元素链对角占优阵或A满足:|α_(ii)‖α_(kk)|>Λ_iΛ_k,i,k∈N={1,…,n},则A为广义对角占优矩阵,detA≠0,揭示了文[3],[4]中detA≠0的共同本     

多重Toeplitz矩阵与多重Hankel矩阵相乘的复杂度

1.二重Toeplitz矩阵相乘的快速算法nm阶方阵 称为nm型2重Toeplitz矩阵,其中A_i(i=-n+1,…,n-1)为m阶Toeplitz矩阵. 定义.设p_1×p_2矩阵A=(a_(ij))_(p_1×p_2),B为q_1×q_2矩阵.称p_1q_1×p_2q_2矩阵     

广义严格对角占优阵的判定程序

1 引言和符号 在本文中,均采用下列符号而不再重申.恒用N表示前n个自然数的集合;而用Mn(C)和Mn(R)分别表示所有n阶复矩阵和所有n阶实矩阵的集合. Z_N={A|A=(a_(ij))_(n×n)∈Mn(R),a_(ij)≤0,i,j∈N,i≠j},I恒表示单位矩阵. 如果A∈Mn(R)且A的所有元素都为非负实数,则称A为非负方阵,并记为A≥0;若A的所有元素都为正数,则称A为正矩阵,并记为A>0. 对A=(a_(ij))(n×n)∈Mn(C),令A_i(A)=sum from j=1 j≠i to n (|a_(ij)|(i=1、2…… n)) ;若把A的非零元用1代替 而得到—个n阶(0,1)矩阵。称为A的导出矩阵。记为;而把A的比较矩阵记为 u(A)=(b_(ij))_(n×n))其中b_(ij)=|a_(ij)|,b_(ij)=-|a_(ij)|(i,j∈N i≠j)     

优势关系下决策表的下近似约简方法研究

研究了优势关系下不协调决策表的下近似约简问题,引入新的下近似约简的定义,证明新的下近似约简与文献[7]定义的下近似约简等价。给出新的下近似约简的判定定理和辨识矩阵,与文献[7]的辨识矩阵相比,计算新的下近似约简的辨识矩阵的时间复杂度要低。因此,可以利用新的辨识矩阵来求决策表的下近似约简．     

ON A CLASS OF NONLINEAR UNIFORM APPROXIMATION PROBLEMS

1　ＩｎｔｒｏｄｕｃｔｉｏｎＬｅｔＸｂｅａｃｏｍｐａｃｔＨａｕｓｄｏｒｆｆｓｐａｃｅ ,ｗｉｔｈＣ(Ｘ)ｔｈｅｓｐａｃｅｏｆｃｏｎｔｉｎｕｏｕｓｆｕｎｃｔｉｏｎｓｄｅｆｉｎｅｄｏｎＸ ,ａｎｄｌｅｔＭ Ｃ(Ｘ)ｂｅｎｏｎｅｍｐｔｙ.ＬｅｔＦ(ｘ ,ｙ) :Ｘ×Ｒ →Ｒｂｅａｎｏｎ ｎｅｇａｔｉｖｅｆｕｎｃｔｉｏｎ ,ａｎｄｃｏｎｓｉｄｅｒｔｈｅｆｏｌｌｏｗｉｎｇｍｉｎｉｍｉｚａｔｉｏｎｐｒｏｂｌｅｍ :ｆｉｎｄｙ∈Ｍｔｏｍｉｎｉｍｉｚｅ‖Ｆ(.,ｙ )‖ ,(1)ｗｈｅｒｅ‖Ｆ (.,ｙ )‖ =ｓｕｐｘ∈ＸＦ(ｘ ,ｙ(ｘ) ) .Ｔｈｅｍ…     

关于weierstrass逼近定理的几点注记

Weierstrass逼近定理是函数逼近论中的重要定理之一,定理阐述了闭区间上的连续函数可以用一多项式去逼近.将该定理进行推广:即使一个函数是几乎处处连续的,也不一定具有与连续函数相类似的逼近性质,但是一个处处不连续的函数却有可能具有这样的性质.证明了定义在闭区间上且与连续函数几乎处处相等的函数具有类似的逼近性质,并给出了weierstrass逼近定理的一个推广应用.     

自适应稀疏伪谱逼近新方法

自适应稀疏伪谱逼近法是广义混沌多项式类方法的最新进展,相对于其它方法具有计算精度高、速度快的优点.但它仍存在如下缺点：1）终止判据对逼近误差的估计精度偏低;2）只适用于单输出问题.本文提出了适用于多输出问题且具有更高逼近精度的自适应稀疏伪谱逼近新方法.本文首先提出了新型终止判据及基于此新型终止判据的自适应稀疏伪谱逼近新方法,并以命题的形式证明了新型终止判据相比于现有终止判据具有更高的估计精度,从而使基于此的逼近函数精度更接近于预期精度;进而,本文基于指标集的统一策略和新型终止判据,提出了适用于多输出问题的自适应稀疏伪谱逼近新方法,该方法因能充分利用各输出变量的抽样结果,具有比将单输出方法直接推广到多输出问题更高的计算效率.多个算例验证了本文所提出新方法的有效性和正确性.     

Saddlepoint Approximations to the Probability of Ruin in Finite Time for the Compound Poisson Risk Process Perturbed by Diffusion

A large deviations type approximation to the probability of ruin within a finite time for the compound Poisson risk process perturbed by diffusion is derived. This approximation is based on the saddlepoint method and generalizes the approximation for the non-perturbed risk process by Barndorff-Nielsen and Schmidli (Scand Actuar J 1995(2):169–186, 1995). An importance sampling approximation to this probability of ruin is also provided. Numerical illustrations assess the accuracy of the saddlepoint approximation using importance sampling as a benchmark. The relative deviations between saddlepoint approximation and importance sampling are very small, even for extremely small probabilities of ruin. The saddlepoint approximation is however substantially faster to compute.     

单隐层神经网络与最佳多项式逼近

研究单隐层神经网络逼近问题．以最佳多项式逼近为度量,用构造性方法估计单隐层神经网络逼近连续函数的速度．所获结果表明:对定义在紧集上的任何连续函数,均可以构造一个单隐层神经网络逼近该函数,并且其逼近速度不超过该函数的最佳多项式逼近的二倍．     

插补空间的逼近外推定理

本文建立了拟模Abelian群上双参数算子族逼近的外推定理,所得的结果包含了DeVoreR．等人对正规逼近族之最佳逼近所建立的外推定理,且所需的条件更弱．同时从本文的结果立即可以建立起算子逼近的外推定理．     

Analysis of the complex moving least squares approximation and the associated element-free Galerkin method

The complex moving least squares approximation is an efficient method to construct approximation functions in meshless methods. This paper begins by analyzing properties, stability and error of the approximation. To overcome the inherent instability, a stabilized approximation is also developed and analyzed. The complex element-free Galerkin method is a meshless method combined with the use of the complex moving least squares approximation. Application of the complex element-free Galerkin method to linear and nonlinear time-dependent problems is then given. Error estimates of the complex element-free Galerkin method are derived theoretically. Numerical examples involving function fitting and solitons are finally provided to show the accuracy and efficiency of the proposed methods.     

Approximation for Navier-Stokes equations around a rotating obstacle

This work presents an approximation method for Navier-Stokes equations around a rotating obstacle. The detail of this method is that the exterior domain is truncated into a bounded domain and a new exterior domain by introducing a large ball. The approximation problem is composed of the nonlinear problem in the bounded domain and the linear problem in the new exterior domain. We derive the approximation error between the solutions of Navier-Stokes equations and the approximation problem.     

Nonlinear Methods of Approximation

Abstract. Our main interest in this paper is nonlinear approximation. The basic idea behind nonlinear approximation is that the elements used in the approximation do not come from a fixed linear space but are allowed to depend on the function being approximated. While the scope of this paper is mostly theoretical, we should note that this form of approximation appears in many numerical applications such as adaptive PDE solvers, compression of images and signals, statistical classification, and so on. The standard problem in this regard is the problem of m -term approximation where one fixes a basis and looks to approximate a target function by a linear combination of m terms of the basis. When the basis is a wavelet basis or a basis of other waveforms, then this type of approximation is the starting point for compression algorithms. We are interested in the quantitative aspects of this type of approximation. Namely, we want to understand the properties (usually smoothness) of the function which govern its rate of approximation in some given norm (or metric). We are also interested in stable algorithms for finding good or near best approximations using m terms. Some of our earlier work has introduced and analyzed such algorithms. More recently, there has emerged another more complicated form of nonlinear approximation which we call highly nonlinear approximation. It takes many forms but has the basic ingredient that a basis is replaced by a larger system of functions that is usually redundant. Some types of approximation that fall into this general category are mathematical frames, adaptive pursuit (or greedy algorithms), and adaptive basis selection. Redundancy on the one hand offers much promise for greater efficiency in terms of approximation rate, but on the other hand gives rise to highly nontrivial theoretical and practical problems. With this motivation, our recent work and the current activity focuses on nonlinear approximation both in the classical form of m -term approximation (where several important problems remain unsolved) and in the form of highly nonlinear approximation where a theory is only now emerging.