基于比例移动最小二乘近似的误差分析

相较于移动最小二乘近似方法,比例移动最小二乘近似法有效地克服了前者带来的矩阵病态这一问题,展示出了更好的数值稳定性和更高的计算精度.给出了比例移动最小二乘近似对函数及其任意阶导数的误差估计,并给出了数值算例来验证之前的理论分析结果,通过与移动最小二乘近似的比较,表明比例移动最小二乘近似能得到更快的收敛性和更稳定的计算性.     

Approximation for bootstrapped empirical processes

We obtain an approximation for the bootstrapped empirical process with the rate of the Komlós, Major and Tusnády approximation for empirical processes. The proof of the new approximation is based on the Poisson approximation for the uniform empirical distribution function and the Gaussian approximation for randomly stopped sums.

     

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…     

Banach空间的p-弱近似性质

给出了Banach空间的p-弱近似性质和p-有界弱近似性质的定义,获得了这些性质的一些刻画.利用这些刻画证明了如果一个Banach空间X的对偶空间X~*有p-弱近似性质(或p-有界弱近似性质),则X有p-弱近似性质(或p-有界弱近似性质),在一般情况下反之不成立.     

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．     

Weak and quasi approximation properties in Banach spaces

In this paper, we introduce weak versions (the weak approximation property, the bounded weak approximation property, and the quasi approximation property) of the approximation property and derive various characterizations of these properties. And we show that if the dual of a Banach space X has the weak approximation property (respectively the bounded weak approximation property), then X itself has the weak approximation property (respectively the bounded weak approximation property). Also we observe that the bounded weak approximation property is closely related to the quasi approximation property.     

Pathwise convergence rates for numerical solutions of Markovian switching stochastic differential equations

This work develops numerical approximation algorithms for solutions of stochastic differential equations with Markovian switching. The existing numerical algorithms all use a discrete-time Markov chain for the approximation of the continuous-time Markov chain. In contrast, we generate the continuous-time Markov chain directly, and then use its skeleton process in the approximation algorithm. Focusing on weak approximation, we take a re-embedding approach, and define the approximation and the solution to the switching stochastic differential equation on the same space. In our approximation, we use a sequence of independent and identically distributed (i.i.d.) random variables in lieu of the common practice of using Brownian increments. By virtue of the strong invariance principle, we ascertain rates of convergence in the pathwise sense for the weak approximation scheme.     

球面带形平移网络逼近的Jackson定理

研究了球面带型平移网络逼近阶用球面调和多项式的最佳逼近及光滑模的刻画问题．借助于球调和多项式的最佳逼近多项式和Riesz平均构造出了单位球面Sq上的带形平移网络,并建立了球面带形平移网络对Lp(Sq)中函数一致逼近的Jackson型定理．所得结果表明球面带形平移网络可以达到球调和多项式的逼近阶．     

Continuous approximation schemes for stochastic programs

One of the main methods for solving stochastic programs is approximation by discretizing the probability distribution. However, discretization may lose differentiability of expectational functionals. The complexity of discrete approximation schemes also increases exponentially as the dimension of the random vector increases. On the other hand, stochastic methods can solve stochastic programs with larger dimensions but their convergence is in the sense of probability one. In this paper, we study the differentiability property of stochastic two-stage programs and discuss continuous approximation methods for stochastic programs. We present several ways to calculate and estimate this derivative. We then design several continuous approximation schemes and study their convergence behavior and implementation. The methods include several types of truncation approximation, lower dimensional approximation and limited basis approximation.His work is supported by Office of Naval Research Grant N0014-86-K-0628 and the National Science Foundation under Grant ECS-8815101 and DDM-9215921.His work is supported by the Australian Research Council.     

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．     

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.     

The weak metric approximation property

We introduce and investigate the weak metric approximation property of Banach spaces which is strictly stronger than the approximation property and at least formally weaker than the metric approximation property. Among others, we show that if a Banach space has the approximation property and is 1-complemented in its bidual, then it has the weak metric approximation property. We also study the lifting of the weak metric approximation property from Banach spaces to their dual spaces. This enables us, in particular, to show that the subspace of c₀, constructed by Johnson and Schechtman, does not have the weak metric approximation property. The research of the second-named author was partially supported by Estonian Science Foundation Grant 5704 and the Norwegian Academy of Science and Letters.     

A numerical approximation of parabolic stochastic partial differential equations driven by a Poisson random measure

The paper deals with SPDEs driven by Poisson random measures in Banach spaces and its numerical approximation. We investigate the accuracy of space and time approximation. As the space approximation we consider spectral methods and as time approximation the implicit Euler scheme and the explicit Euler scheme. AMS subject classification (2000) 60H15, 35R30     

伪多元函数的Padé型逼近

给出伪多元函数的Pad\'{e}型逼近,其优点在于:简化了多元函数有理逼近的计算,加速了函数在奇点处的收敛.并讨论其性质以及误差分析.     

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．     

模糊粗糙近似算子公理集的独立性

用双论域上的模糊关系定义了广义模糊粗糙近似算子,并讨论了近似算子的性质。用公理刻画了模糊集合值算子,各种公理化的近似算子可以保证找到相应的二元模糊关系,使得由模糊关系通过构造性方法定义的模糊粗糙近似算子恰好就是用公理定义的近似算子。讨论了刻画各种特殊近似算子的公理集的独立性,从而给出各种特殊模糊关系所对应的模糊粗糙近似算子的最小公理集。     

Nonlinear Approximation with Dictionaries I. Direct Estimates

We study various approximation classes associated with m-term approximation by elements from a (possibly redundant) dictionary in a Banach space. The standard approximation class associated with the best m-term approximation is compared to new classes defined by considering m-term approximation with algorithmic constraints: thresholding and Chebychev approximation classes are studied, respectively. We consider embeddings of the Jackson type (direct estimates) of sparsity spaces into the mentioned approximation classes. General direct estimates are based on the geometry of the Banach space, and we prove that assuming a certain structure of the dictionary is sufficient and (almost) necessary to obtain stronger results. We give examples of classical dictionaries in L^p spaces and modulation spaces where our results recover some known Jackson type estimates, and discuss some new estimates they provide.     

模糊粗糙群

研究模糊群的粗糙近似。借助模糊正规子群生成群上模糊近似空间,在模糊粗糙集模型的框架中对相关的模糊粗糙近似算子进行讨论。得到了模糊粗糙近似算子的基本性质,并研究了模糊粗糙近似算子与群中乘法运算的相容性。     

一类修正的多元和型积分算子的逼近等价定理

本文引进了一类重要的多元和型积分算子，它们具有保线性，讨论了它们的逼近性质。利用插补空间方法，建立了多元和型积分算子逼近的正定理和逆定理，给出了其逼近度与函数光滑性间的关系。     

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.