AR(1)序列的样本均值分布的随机加权逼近

随机加权方法是郑忠国近期提出的逼近统计量分布的新方法.目前,已有许多文章讨论了此方法对一些统计量分布的逼近问题.但这些结果仅限于对独立随机样本的研究.本文将此方法应用于相依随机序列——AR(1)序列的样本均值,并得到了和独立情形同样好的结果.     

随机规划ε-逼近最优解集的Hausdorff收敛性

本文研究了随机规划ε-逼近最优解集的Haudorff收敛性条件,证明了随机规划逼近最优值的收敛性,并利用此结果给出了随机规划ε-逼近最优解集Haudorff收敛的一个充分条件.     

一个新锥模型信赖域算法

1引言本文考虑的无约束最优化问题为(?)f(x),(1.1)其中f(x)为连续可微函数.解此问题的很多算法一般都采用二次函数模型去逼近f(x) ([10],[15]).对于一些非二次性态强、曲率变化剧烈的函数,用二次函数模型去逼近可能效果不好,因此Davidon于1980年首次提出了解无约束优化问题的锥模型方法.锥模型是二次模型的推广,比二次函数具有更多的自由度,因此期望能够更充分地逼近原函数.对于一些在极小点附近很不对称,或曲率变化剧烈的函数,或在某个区域内变化大的函数,全部或部分用锥模型去逼近的效果可能好于用二次模型去逼近.     

某些数的联立逼近

本文研究了同G函数有关的联立逼近.从而得出,某些方程的根不能用代数数逼近的“太好”和G函数值的代数无关度量.     

象征算子的紧算子逼近算法和逼近速度

传统的微分方程的方法是利用Taylor展开用主象征逼近象征;本文用充分好的紧算子来逼近象征算子,并且逼近算子在算子范数意义下快速逼近原来的算子.     

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.     

半线性椭圆问题Petrov-Galerkin逼近及亏量迭代

本文考虑了二阶半线性椭圆问题的Petrov-Galerkin逼近格式,用双二次多项式空间作为形函数空间,用双线性多项式空间作为试探函数空间,证明了此逼近格式与标准的二次有限元逼近格式有同样的收敛阶.并且根据插值算子的逼近性质,进一步证明了半线性有限元解的亏量迭代序列收敛到Petrov-Galerkin解.     

双调和方程混合元的一种新格式

本文介绍了双调和方程混合元的一种新格式,用双二次多项式逼近流函数,双一次多项式逼近涡函数.在拟一致矩形剖分的条件下,证明了此格式具有与C-R格式中分别用双二次多项式逼近相同的收敛阶.     

一种方便实用的有理逼近及其对于大量优化方法的改进

本文提出了适于改善大量优化算法的一种有理逼近,该逼近利用 n 维空间中两点的一阶以下的信息.该逼近由于属于超曲而,比作为超平面的一阶 Taylor 展式要逼近得好,只是利用了上次迭代信息.该逼近不必增加计算工作量,属于信息的重复带来的效益,是值得采用的.又因为其分子,分母皆为稍加改动而不进行实质性的变化,却可提高问题的求解效率.本文对于该有理逼近的提出进行了数学上的推导论证,叙述了应用该逼近对大量优化方法和线性迭代算法的改进,进行了公式推     

球面广义移动最小二乘的diffuse泛函逼近

考虑单位球面上广义移动最小二乘的逼近问题.用一般意义的泛函代替标准移动最小二乘中的点值泛函,给出单位球面上广义移动最小二乘的定义,并在此基础上通过diffuse导数的概念定义一种diffuse泛函的移动最小二乘逼近,根据球面网格范数给出逼近阶估计.该diffuse泛函移动最小二乘逼近亦可被理解为一种"同时"逼近.     

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．     

On 3-monotone approximation by piecewise polynomials

We consider 3-monotone approximation by piecewise polynomials with prescribed knots. A general theorem is proved, which reduces the problem of 3-monotone uniform approximation of a 3-monotone function, to convex local L₁ approximation of the derivative of the function. As the corollary we obtain Jackson-type estimates on the degree of 3-monotone approximation by piecewise polynomials with prescribed knots. Such estimates are well known for monotone and convex approximation, and to the contrary, they in general are not valid for higher orders of monotonicity. Also we show that any convex piecewise polynomial can be modified to be, in addition, interpolatory, while still preserving the degree of the uniform approximation. Alternatively, we show that we may smooth the approximating piecewise polynomials to be twice continuously differentiable, while still being 3-monotone and still keeping the same degree of approximation.     

Banach 空间中非线性最佳逼近的广义强唯一性

设X是一致凸空间,G为X中太阳集,R．Smarzewski[1]证明了g∈G对x∈的最佳逼近具有广义强唯一性,本文讨论其逆,在最佳逼近是广义强唯一的条件下,研究了空间的凸性和逼近集的太阳性．     

Approximation Bounds for Trilinear and Biquadratic Optimization Problems Over Nonconvex Constraints

This paper presents new approximation bounds for trilinear and biquadratic optimization problems over nonconvex constraints. We first consider the partial semidefinite relaxation of the original problem, and show that there is a bounded approximation solution to it. This will be achieved by determining the diameters of certain convex bodies. We then show that there is also a bounded approximation solution to the original problem via extracting the approximation solution of its semidefinite relaxation. Under some conditions, the approximation bounds obtained in this paper improve those in the literature.     

Bounds and an isotropically self-consistent singular approximation of the linear elastic properties of cubic crystal aggregates for application in materials design

For crystal aggregates, the orientation distribution of single crystals affects the anisotropic linear elastic properties. In the singular approximation for cubic materials, this influence is reflected by a fourth-order texture coefficient. From this approximation, the statistical bounds of Voigt, Reuss and Hashin-Shtrikman, and an isotropically self-consistent singular approximation can be obtained. Here, an approximation is called isotropically self-consistent, if, for a vanishing texture, it results in the isotropic self-consistent approximation. The isotropically self-consistent singular approximation has the following advantages: i) it lies between the bounds of Voigt, Reuss and Hashin-Shtrikman, ii) it offers a useful approximation of the effective material behavior of textured anisotropic polycrystals, and iii) it can be used for material design purposes tailoring anisotropic properties mainly depending on the crystallographic texture. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Upper and lower approximation models in interval regression using regression quantile techniques

In this paper, we propose new interval regression analysis based on the regression quantile techniques. To analyze a phenomenon in a fuzzy environment, we propose two interval approximation models. Without using all data, we first identify the main trend from the designated proportion of the given data. To select the main part of data to be analyzed, we introduce the regression quantile techniques. The obtained model is not influenced by extreme points since it is formulated from the center-located main proportion of the given data. After that, the interval regression model including all data can be identified based on the acquired main trend. The obtained interval regression model by the main proportion of the given data is called the lower approximation model, while interval regression model by all data is called the upper approximation model for the given phenomenon. Also it is shown that, from the lower approximation model (main trend) and the upper approximation model, we can construct a trapezoidal fuzzy model. The membership function of this fuzzy model is useful to obtain the locational information for each observation. The characteristic of our approach can be described as obtaining the upper and lower approximation models and combining them to be a fuzzy model for representing the given phenomenon in a fuzzy environment.     

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．     

Approximation by Shepard type pseudo-linear operators and applications to Image Processing

Recently, it has been shown that sum and product are not the only operations that can be used in order to define concrete approximation operators. Several other operations provided by fuzzy sets theory can be used. In the present paper, pseudo-linear approximation operators are investigated from the practical point of view in Image Processing. We study max–min, max–product Shepard type approximation operators together with Shepard operators based on pseudo-operations generated by an increasing continuous generator. It is shown that in several cases these outperform classical approximation operators based on sum and product operations.     

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.     

扰动Fej\'{e}r点上复Hermite插值的联合逼近

$D$是复平面中由闭Jordan曲线$\Ga$围成的单连区域. 考虑在$\Ga$上扰动Fej\''er点的 Hermite插值一致逼近、平均逼近和联合逼近于函数$f\in A^{(q)}(\o D)$. 该文中的逼近阶一般说来是不可再改进的, 区域的边界限制条件到目前为止是最少的. 以往的全部同类结果都包括在该文中作为特殊情形, 由于该文方法上的改进, 简化和省去了以往 某些证明过程.