CM分担两个小函数且IM分担另一个小函数的亚纯函数

本文研究了两个亚纯函数共享两个CM小函数和一个I M小函数,并存在第四个小函数和一定条件下的唯一性.利用构造辅助函数方法,得到一个亚纯函数是另一个亚纯函数的拟分式线性变换.     

左截断相依数据下非参数回归的局部M 估计

本文对左截断模型, 利用局部多项式的方法构造了非参数回归函数的局部M 估计. 在观察样本为平稳α-混合序列下, 建立了该估计量的强弱相合性以及渐近正态性. 模拟研究显示回归函数的局部M 估计比Nadaraya-Watson 型估计和局部多项式估计更稳健.     

M(C_n)和M(W_n)图的邻点可区别的Ⅰ-全色数

研究了M(C_n)和M(W_n)图的邻点可区别的I-一全染色.根据M(C_n)和M(W_n)图的构造特征,利用构造函数法,构造了一个从点边集V(G)∪E(G)到色集合{1,2,…,k)的函数,给出了一种染色方案,得到了它们的邻点可区别的I-全色数.     

M 序列反馈函数的构造方法Ⅲ

二元域 F_2上周期为2~n 的 n 级 M 序列(即最大长度移位寄存器序列)具有较好的随机性.实际构造这种序列及其反馈函数(简称 M 馈)历来很受重视.近几年来,人们试图从一线性移位寄存器出发来构造 M 馈.利用一个 n 次本原多项式,J.Mykkeltveit 等人构造了2~n-2个 n+1级 M 馈,M.K.siu 与 P.Tong 构造出2~(n+1)个 n+2级 M 馈,F.Hemmati 又构造出2~(5n)个 n+2级 M 馈.B.Arozi 用两个次数分别为 m_1和 m_2的本原多项式构造出一个 m_1+m_2级 M 馈,其中(m_1,m_2)=1.我们在[1]和[2]中提供了几种直接构造 M 馈的方法,从任一非奇异移存器出发,可以直接写出一大批 M 馈.本文是[1]和[2]的继续,通过对几类线性移存器因子关联图的详细分析,构造出几类新的 M 馈.在§2中,用两个互反的 n 次本原多项式构造出2~(?)(2~(n-2)-     

求解非线性互补问题一个新的Jacobian光滑化方法

本文构造了非线性互补问题一个新的光滑逼近函数,分析了该函数的一些基本性质.利用这一新的光滑逼近函数建立了求解非线性互补问题的一个Jacobi光滑化方法,并证明了在适当的条件下这一算法是全局及局部超线性收敛的.数值结果表明该方法是有效的.     

一类Toeplitz线性代数方程组的预处理GMRES方法

本文研究一类来源于分数阶特征值问题的Toeplitz线性代数方程组的求解.构造Strang循环矩阵作为预处理矩阵来求解该Toeplitz线性代数方程组,分析了预处理后系数矩阵的特征值性质.提出求解该线性代数方程组的预处理广义极小残量法（PGMRES）,并给出该算法的计算量.数值算例表明了该方法的有效性.     

紧黎曼面上一类线丛截面空间的基

对于亏格g≥1的紧Riemann曲面M,本文运用Riemann-Roch定理,证明了其上线丛Lλ(M)的某类截面空间非空集,并利用Theta函数和素形式构造出这类截面空间的一组基．     

受限制多项式插值及在构造形函数空间中的应用

1引言 G.Strang指出：有限元法的新思想在于试控函数的选择，目标是选择这样的分片多项式，它们被少数几个方面的节点值确定，并仍具有我们需要的连续性和逼近度。受限制多项式插值空间就是这样一类空间，在P.G.Ciarlet~[4]的书中有较多的介绍，采用的方法是通过约束条件来决定试验空间，但正如[1]中指出的，这样约束条件欠直观，且容易产生一些不确     

多元(M,R)-插值型可加细函数向量的构造

本文提出了一个全新的具有r个分量函数的多元插值型可加细函数向量,即(M,R)-插值型可加细函数向量,这里M是膨胀矩阵,r=|detR|.基于(M,R)-插值型尺度滤波器,我们详细地刻画了(M,R)-插值型可加细函数向量的性质,并得到了尺度滤波器满足k+1阶和规则的充分必要条件.此外,为获得具有对称性的(M,R)-插值型可加细函数向量,我们还给出了相应尺度滤波器的结构.围绕上述理论结果,在本文的最后,我们给出了若干数值构造实例.     

例说构造差(和)函数用导数解题

运用导数解题时,常常需要构造辅助函数,而构造差(和)函数是构造辅助函数的主要形式,下面就构造差(和)函数利用导数解题的常见类型予以介绍,供大家参考.     

锥中与稳态的薛定谔算子相关的广义Martin函数的控制

In this paper we show that a positive superfunction on a cone behaves regularly at infinity outside a minimally thin set associated with the stationary Schr(o|¨)dinger operator.     

A new factorization technique of the matrix mask of univariate refinable functions

Summary. A univariate compactly supported refinable function can always be written as the convolution product , with the B-spline of order k,f a compactly supported distribution, and k the approximation orders provided by the underlying shift-invariant space . Factorizations of univariate refinable vectors were also studied and utilized in the literature. One of the by-products of this article is a rigorous analysis of that factorization notion, including, possibly, the first precise definition of that process. The main goal of this article is the introduction of a special factorization algorithm of refinable vectors that generalizes the scalar case as closely (and unexpectedly) as possible: the original vector is shown to be `almost' in the form , with F still compactly supported and refinable, andk the approximation order of . The algorithm guarantees F to retain the possible favorable properties of , such as the stability of the shifts of and/or the polynomiality of the mask symbol. At the same time, the theory and the algorithm are derived under relatively mild conditions and, in particular, apply to whose shifts are not stable, as well as to refinable vectors which are not compactly supported. The usefulness of this specific factorization for the study of the smoothness of FSI wavelets (known also as `multiwavelets' and `multiple wavelets') is explained. The analysis invokes in an essential way the theory of finitely generated shift-invariant (FSI) spaces, and, in particular, the tool of superfunction theory. Received June 10, 1998 / Revised version received June 14, 1999 / Published online August 2, 2000     

An two phase abs method for solving over determined systems of linear inequalities

The method, called the Multi-Stage ABS algorithm, for solving the over-determined linear inequalities system and the system combined with the over-determined linear inequalities and the equations is presented. This method is characterized by translating inequalities system to an equations system with slack variables. The explicit solution with the slack variables of the equations system are given by the implicit LU algorithm, then the slack variables can be given by the ABS algorithm. Finally, the upper multiplications of the algorithm are given.     

最短时限缺省指派问题的一种解法

将周良泽在 1998年提出的最短时限缺省指派问题转化成赋权二分图的最小权 K-匹配问题。研究了其解的最优性充分及必要条件 ,并给出了适合在图上求解的生长树法及适合在表上直接求解的标号法 ,最后给出一个实例。该解法是一种较简便的算法。     

进化泛函网络多维函数逼近理论及学习算法

提出了一种用于多维函数逼近的进化策略修正泛函网络基函数系数的新算法,并给出了其算法学习过程.利用进化策略的自适应性来确定基函数前的系数,改进了泛函网络的参数通过解方程组来得到这一传统方法.仿真结果表明,这种新的逼近算法简单可行,能够逼近给定的函数到预先给定的精度,具有较快的收敛速度和良好的逼近性能.     

Discrete allocation of a linear resource according to a sum-of-products objective

The allocation of a linear resource according to the sum of the returns from independent activities is considered. The return from each activity is given by a product of concave and nondecreasing functions of a single allocation variable. The model can be used, for instance, to describe probabilities of success of several serial tasks, into which an activity is subdivided. An incremental algorithm is defined and conditions are given for the algorithm to generate an optimal solution; otherwise, the problem is solved by a two-step procedure involving the incremental maximization of the return corresponding to a single activity and the combination of the activities by dynamic programming. Examples are given of problems solvable and not solvable by the incremental algorithm.     

Incremental allocation of a discrete and convex resource to concave activities

The problem considered is that of the allocation of a given quantity of a discrete resource to activities described by concave return functions. The resource-consumption corresponding to each allocation is described by a convex function of the quantity allocated. An incremental solution algorithm is given, which specializes to the algorithm of Fox if the resource is linear, and to an algorithm of Katoh, Ibaraki and Mine if the return functions are linear.     

UPGMA clustering revisited: A weight-driven approach to transitive approximation

A new algorithm is proposed for generating min-transitive approximations of a given similarity matrix (i.e. a symmetric matrix with elements in the unit interval and diagonal elements equal to one). Different approximations are generated depending on the choice of an aggregation operator that plays a central role in the algorithm. If the maximum operator is chosen, then the approximation coincides with the min-transitive closure of the given similarity matrix. In case of the arithmetic mean, a transitive approximation is generated which is, on the average, as close to the given similarity matrix as the approximation generated by the UPGMA hierarchical clustering algorithm. The new algorithm also allows to generate approximations in a purely ordinal setting. As this new approach is weight-driven, the partition tree associated to the corresponding min-transitive approximation can be built layer by layer. Numerical tests carried out on synthetic data are used for comparing different approximations generated by the new algorithm with certain approximations obtained by classical methods.     

二元有理插值的迭加算法

在已有基础上给出一种新的算法,即迭加插值算法,并给出相应的插值有理函数的具体表达式,以及与已有算法比较,该算法具有较大的灵活性,更便于实际应用.     

Stable computation of solutions of unstable linear initial value recursions

An algorithm is given for approximating dominated solutions of linear recursions, when some initial conditions are given. The stability of this algorithm is investigated and expressions for the truncation and rounding errors are derived. A number of practical questions concerning the algorithm is considered, and several numerical examples sustain the theory.