小波树逼近及其应用

利用“构造性贪婪算法(CGS)”构造目标函数的小波树逼近. 首先定义了一个函数类, 对此函数类中的每个函数, 由CGS生成的分片多项式逼近都具有给定的收敛阶. 其次通过研究所定义函数类的嵌入性质讨论了该函数类和其他已知函数空间的关系. 在小波树逼近领域, 给出了使小波树逼近达到最优收敛阶的一个充分条件. 最后证明, 如果树结构是用CGS生成的, 则相应的小波树逼近具有最优收敛阶.     

极大值函数的一类光滑逼近函数的性质研究

针对极大值函数的一类光滑逼近——凝聚函数,对其作进一步研究.指出凝聚函数的一阶导数对光滑参数取极限时恰好得到极大值函数的一个次梯度,从而凝聚函数不仅可以一致逼近极大值函数,而且该函数富含极大值函数的一阶信息,可很好的刻画极大值函数的一阶特征.进一步,对光滑逼近函数的光滑参数做简单分析,得到的结果揭示了光滑参数的变动对凝聚函数的影响.并分别以正值函数及绝对值函数为例,对所得到的结果给出几何说明.     

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.     

由斜坡函数激发的神经网络算子逼近

首先,引入一种由斜坡函数激发的神经网络算子,建立了其对连续函数逼近的正、逆定理,给出了其本质逼近阶.其次,引入这种神经网络算子的线性组合以提高逼近阶,并且研究了这种组合的同时逼近问题.最后,利用Steklov函数构造了一种新的神经网络算子,建立了其在L~p[a,b]空间逼近的正、逆定理.     

用叠二次样条插值逼近导函数

在第一种边界条件下,我们证明了二重、三重、四重叠二次样条插值以h_2的精度分别逼近光滑函数的一阶、二阶、三阶导数,并说明对适当提出的边界条件.叠二次样条插值可以在h_2的精度范围内逼近光滑函数的其它阶导数.     

Bernstein-Durrmeyer算子的加权同时逼近等价定理

本文借助修正的Voronovskaja定理,用更一般的光滑模函数来刻划加权同时逼近阶,建立了加权同时逼近等价定理,统一了非最优逼近阶及饱和阶的特征刻划.     

紧区间上高斯核近似逼近误差估计的改进

本文研究了由高斯核构成的拟插值算子在闭区间上的近似逼近问题.利用函数延拓和近似单位分划的方法,构造了拟插值算子,并得到了一致范数下的逼近阶估计.     

Orlicz空间内一类有理函数逼近的一种Jackson型估计

研究了Orlicz空间内一类有理函数逼近问题.在被逼近函数改变l次符号的条件下,借助Steklov平均函数,利用修正的Jackson核,Hardy-Littlewood极大函数,Cauchy-Schwarz不等式等工具,给出了逼近阶的一种Jackson型估计.考虑到Orlicz空间内拓扑结构的复杂性,本文得到的结果比连续函数空间和L_p空间内同类问题的研究结果具有更广泛的意义.     

Lagrange插值在—重积分Wiener空间下的同时逼近平均误差

在加权L_p范数逼近意义下,确定了基于扩充的第二类Chebyshev结点组的Lagrange插值多项式列,在一重积分Wiener空间下同时逼近平均误差的渐近阶.结果显示,在L_p范数逼近意义下,Lagrange插值多项式列逼近函数及其导数的平均误差都弱等价于相应的最佳逼近多项式列的平均误差.同时,在信息基复杂性的意义下,若可允许信息泛函为标准信息,则上述插值算子列逼近函数及其导数的平均误差均弱等价于相应的最小非自适应信息半径.     

K 泛函与逼近阶(Ⅰ)

<正> K 泛函的概念首先是由 Peetre 引入的.DeVore 等利用 K 泛函这一工具成功地得到了各种用函数的光滑模来表示的逼近阶.本文的目的是要推广这一工具,使它可以用来考察函数及其导函数的同时逼近(以下简称同时逼近)问题.我们利用这一推广了的工具,对于用样条、三角多项式以及代数多项式线性同时逼近的阶,得到了比较完善的结果.     

Approximations of choice probabilities in mixed logit models

This paper is concerned with the approximate computation of choice probabilities in mixed logit models. The relevant approximations are based on the Taylor expansion of the classical logit function and on the high order moments of the random coefficients. The approximate choice probabilities and their derivatives are used in conjunction with log likelihood maximization for parameter estimation. The resulting method avoids the assumption of an apriori distribution for the random tastes. Moreover experiments with simulation data show that it compares well with the simulation based methods in terms of computational cost.     

Quantitative Approximation to the Ordered Dirichlet Distribution under Varying Basic Probability Spaces

An approximate expansion of a sequence of ordered Dirichlet densities is given under the set-up with varying dimensions of the relating basic probability spaces. The problem is handled as the approximation to the joint distribution of an increasing number of selected order statistics based on the random sample drawn from the uniform distribution U(0, 1). Some inverse factorial series to the expansion of logarithmic function enable us to give quantitative error evaluations to our problem. With the help of them the relating modified K-L information number, which is defined on an approximate main domain and different from the usual ones, is accurately evaluated. Further, the proof of the approximate joint normality of the selected order statistics is more systematically presented than those given in existing works. Concerning the approximate normality the modified affinity and the half variation distance are also evaluated.     

<Emphasis Type="Italic">α</Emphasis>-Conservative approximation for probabilistically constrained convex programs

In this paper, we address an approximate solution of a probabilistically constrained convex program (PCCP), where a convex objective function is minimized over solutions satisfying, with a given probability, convex constraints that are parameterized by random variables. In order to approach to a solution, we set forth a conservative approximation problem by introducing a parameter α which indicates an approximate accuracy, and formulate it as a D.C. optimization problem.     

On the maximum deviation in random walks

We consider a random walk with drift to the left. LetM _n denote the extreme position to the right of the particle during its firstn steps. An approximate expression for the characteristic function of the distribution of this random variable is evaluated. The numerical inversion of this characteristic function is performed with the aid of the Fast Fourier Transform.     

基于梯型密度函数的连续分布随机数近似生成方法

讨论了产生连续分布随机数的近似方法,给出了基于梯型密度函数表示的连续分布随机数近似算法,应用近似算法给出了几种常见类型随机数的实验结果．     

On the safety stock problem for random delivery processes

Inventory models are considered in which the delivery of an order occurs not on one occasion but at random moments of a period in random parts. We give two extensions of the reliability type inventory model of A. Prékopa. In this model a known constant demand rate is assumed and a simple approximate formula is given for the initial stock of the order period which serves as safety stock and ensures a continuous supply during the whole order period on a prescribed probability level. This formula is widely used in practice for safety stock planning in the case when deliveries in random parts occur.We formulate a generalized version of the random delivery process and derive the exact solution of the safety stock which can be applied also for the previous model. In the second model a random demand rate is considered together with a random delivery process. An exact solution method and a simple approximate formula for the safety stock will be discussed. We have experiences in the application of these models both in a steel works and a textile factory in Hungary.     

Importance Sampling for p-value Computations in Multivariate Tests

Abstract

An importance sampling procedure is developed to approximate the distribution of an arbitrary function of the eigenvalues for a matrix beta random matrix or a Wishart random matrix. The procedure is easily implemented and provides confidence intervals for the p-values of many of the commonly used test statistics in multivariate analysis. An adaptive procedure allows for the control of either absolute error or relative error in this p-value estimation through the choice of importance sample size.     

A parallelizable dynamic fleet management model with random travel times

In this paper, we present a stochastic model for the dynamic fleet management problem with random travel times. Our approach decomposes the problem into time-staged subproblems by formulating it as a dynamic program and uses approximations of the value function. In order to deal with random travel times, the state variable of our dynamic program includes all individual decisions over a relevant portion of the history. We show how to approximate the value function in a tractable manner under this new high-dimensional state variable.Under our approximation scheme, the subproblem for each time period decomposes with respect to locations, making our model very appealing for large-scale applications. Numerical work shows that the proposed approach provides high-quality solutions and performs significantly better than standard benchmark methods.     

基于包含度的模糊随机粗糙集模型

针对随机性与模糊性同时存在的情形,提出了建立在模糊随机近似空间上的基于包含度的模糊随机粗糙集模型.首先给出了模糊随机近似空间的概念,然后利用包含度提出了模糊随机近似空间上的一种基于模糊随机集的粗糙近似算子.最后讨论了这种近似算子的一些性质.     

Stability results for stochastic programming problems

The paper deals with a statistical approach to stability analysis in nonlinear stochastic programming. Firstly the distribution function of the underlying random variable is estimated by the empirical distribution function, and secondly the problem of estimated parameters is considered. In both the cases the probability that the solution set of the approximate problem, is not contained in an l-neighbourhood of the solution set to the original problem is estimated, and under differentiability properties an asymptotic expansion for the density of the (unique) solution to the approximate problem is derived.