含弥散核废料污染问题有限元方法的收敛性

１引言核废料污染的物理过程包括四个方面：（１）流体流动,（２）热量迁移,（３）主要污染元素的相混溶驱动,（４）微量元素的相混溶驱动．其数学模型为耦合拟线性抛物型方程组的初边值问题［２．５］．在前期数值分析工作中,［３,４］研究了不可压缩问题的有限元混合元等方法,［２,７,８］研究了可压缩问题的有限元混合元等方法,其收敛性分析限于仅考虑分子扩散而忽略弥散的情形．［９］研究了可压缩问题,考虑分子扩散和弥散,对原始数学模型进行了转化和变形,给出了有限元格式和Ｌ２（Ｊ;Ｈ１（））收敛性．本文进一步研究包…     

Stokes问题混合元方法中数值积分的影响

１引言用混合有限元方法求解Ｓｔｏｋｅｓ问题的近似解时,有两点重要事项．其一是众所周知的稳定化条件,这就导致在一阶及二阶格式中引人水泡（ｂｕｂｂｌｅ）函数（参见［１］,［２］,［４］和［６］）．其二是在实际计算中,含有体力密度ｆ的积分,需代之以数值积分,而保持原有的逼近阶不变．值得指出的是。该数值积分与水泡（ｂｕｂｂｌｅ）函数项无关．考虑数值积分下的Ｓｔｏｋｅｓ问题混合元逼近：设给定空间其中,是有界连通的多边形区域．考虑Ｓｔｏｋｅｓ问题混合变分形式：求Ｖ,ｐ　Ｍ,使得其中表示Ｌ２内积．众所周知,（…     

强混合序列部分和的强逼近

具有 φ（ｘ）一致可积的混合序列的强逼近问题（其中 φ（ｘ）／ｘ２＋ δ↑∞,δ＞ ０）是本文所要论述的主题．文章给出的结论弥补了［１］中强混合序列的强逼近与独立序列之间的空隙,同时推广了［１］中的结论     

一个第二类变分不等式的有限元逼近

本短文讨论下述第二类变分不等式（见 ［２, ４］）的有限元逼近及其误差分析：其中是平面凸多边形区域的的边界, 且而 ． 诸如热量控制问题,流体通过半可透性壁的扩散问题以及简化库仑摩擦接触问题的正则化方法等均可归为上述变分不等式（１）（见［２,３］）．在文［２］中给出了上述变分不等式的有限元逼近格式,作出了收敛性分析及误差估计．本文的目的是进一步用数值积分简化上述有限元逼近格式并改进原有的估计误差． 设Ｔｈ是的拟一致三角形部分,Ｖｈ是对应的线性元空间,且使得ｖｈ＝０在上．［２］中用数值积分代替其中 Ｍｉ…     

二维抛物型积分微分方程动边界问题的有限元方法

１引言抛物型积微分方程,可广泛用于描述具有记忆的材料的热传导、气体扩散、松散介质中的压力等实际问题中的现象,具有重要研究意义．关于固定空间区域上该类方程的研究,可见文献［１］,［２］;关于动边界抛物型方程,梁国平等已有重要工作［３］,［４］;作者在文［５］中,研究了一维动边界抛物型积微分方程的数值方法．本文研究二维空间区域变动情形下此类方程初边值问题的全离散、半离散有限元逼近格式及有关数值分析．主要特点在于对动边界和时间积分项（Ｖｏｌｔｅｒｒａ项）的处理．对于前者,通过空间变量代换,将问题化为定…     

有限元的一个局部超收敛结果

１．引 言 设 是一个有界开域，具充分光滑的边界 且设 是 上的一族拟一致的三角剖分，用 表示定义在Ｔｈ上的分片线性有限元空间，并置考虑模型问题 用 分别表示的有限元解及内插，那么有插值估计：（见［１］）一般地，如ｕ为问题（１．１）的解，我们有有限元逼近误差估计（见［３］） 命题１．设 并设 分别表示按定义的Ｇｒｅｅｎ函数及其有限元逼近，那么有其中 Ｃ与 ｚ，ｈ无关．（参见［３］） 注意．如 且 ，那么至少存在一个点 ，使即ｘ０是ｆ的奇点，例如其中 为常数， ，显然如果。，如果故我们假定 本文将证明，误差与ｆ的奇性…     

定常的磁流体动力学问题的Galerkin-Petrov最小二乘混合元方法

提出了定常的磁流体动力学方程的一种Galerkin-Petrov最小二乘混合元法,并导出Galerkin-Petrov最小二乘混合元解的存在性和误差估计．通过引入Galerkin-Petrov最小二乘混合有限元方法使得该方法的混合元空间之间的组合无需满足离散的Babuska-Brezzi稳定性条件,从而使得它们的混合有限元空间可以任意选取,并得到误差估计最优阶．     

二阶椭圆问题的混合元方法

１．引 言 令 是有界区域,边界 充分光滑．Ｓｏｂｏｌｅｖ空间 是熟知的．引入Ｑ＝ Ｈ（ｄｉｖ;Ω）,Ｕ＝ Ｈ１（Ω）,内积和范数记为而 是 的半范．令 ,其范数为 ． 考虑如下二阶椭圆问模型题：由问题（０．１）的位移有限元解通过求导的方法来求ｐ的近似解,会带来额外的舍入误差．应用Ｂａｂｕｓｋａ－Ｂｒｅｚｚｉ混合元法［２］则可得到ｐ足够精度的逼近解．但是,该方法要求离散Ｋ－椭圆性和Ｉｎｆ－Ｓｕｐ不等式同时成立,使得混合元的构造或自由度的选取变得相当复杂［２,１２－１４］．通过“增补”办法,能够克服Ｋ－椭圆性…     

退化椭圆问题的最小二乘混合有限元方法

本文研究了电磁场中关于共振现象的一类退化的椭圆问题 ,提出了最小二乘混合有限元方法 .这一方法的好处是可以去掉传统混合元空间的LBB条件所得到的系数矩阵是对称正定的 ,使得法语解更加方便 .本文得到了最小二乘混合有限元方法的L2 和H1估计 .     

板问题多重网格法收敛性的低模估计

１．引言 近年来,多重网格法已成为行之有效的偏微分方程数值解法．对板问题有限元离散系统的多重网格法,也有不少的研究工作,如［４］,［５］,［１０］,［１３－１７］．在［４］,［１４－１７］中,作者讨论了Ｃ１协调元离散板问题的多重网格法,并在能量模（即 Ｈ２模）意义下获得了最优的收敛率．在［５］,［１０］中,作者讨论了非协调元离散问题的多重网格法,并在能量模意义下获得了最优的收敛率,同时在能量模意义下证明了套迭代多重网格法一阶收敛．但对板问题多重网格法的低模估计,即 Ｈ１模估计,至今尚未见研究,本文…     

一类Bernstein型算子加权逼近

本文首先给出了一类用递归法定义的Ｂｅｒｎｓｅｉｎ型算子在一致逼近意义下的特征刻划,然后指出在通常的加权范数下,它虹无界的,通过引入的一种新范数,我们给出了该算子加Ｊａｃｏｂｉ权逼近的特征刻划。     

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．     

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.     

On Martingale Approximation of Adapted Processes

We show that the existence of a martingale approximation of a stationary process depends on the choice of the filtration. There exists a stationary linear process which has a martingale approximation with respect to the natural filtration, but no approximation with respect to a larger filtration with respect to which it is adapted and regular. There exists a stationary process adapted, regular, and having a martingale approximation with respect to a given filtration but not (regular and having a martingale approximation) with respect to the natural filtration.     

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．     

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.     

Convergence Rates of Adaptive Methods,Besov Spaces,and Multilevel Approximation

This paper concerns characterizations of approximation classes associated with adaptive finite element methods with isotropic h-refinements. It is known from the seminal work of Binev, Dahmen, DeVore and Petrushev that such classes are related to Besov spaces. The range of parameters for which the inverse embedding results hold is rather limited, and recently, Gaspoz and Morin have shown, among other things, that this limitation disappears if we replace Besov spaces by suitable approximation spaces associated with finite element approximation from uniformly refined triangulations. We call the latter spaces multievel approximation spaces and argue that these spaces are placed naturally halfway between adaptive approximation classes and Besov spaces, in the sense that it is more natural to relate multilevel approximation spaces with either Besov spaces or adaptive approximation classes, than to go directly from adaptive approximation classes to Besov spaces. In particular, we prove embeddings of multilevel approximation spaces into adaptive approximation classes, complementing the inverse embedding theorems of Gaspoz and Morin. Furthermore, in the present paper, we initiate a theoretical study of adaptive approximation classes that are defined using a modified notion of error, the so-called total error, which is the energy error plus an oscillation term. Such approximation classes have recently been shown to arise naturally in the analysis of adaptive algorithms. We first develop a sufficiently general approximation theory framework to handle such modifications, and then apply the abstract theory to second-order elliptic problems discretized by Lagrange finite elements, resulting in characterizations of modified approximation classes in terms of memberships of the problem solution and data into certain approximation spaces, which are in turn related to Besov spaces. Finally, it should be noted that throughout the paper we paid equal attention to both conforming and non-conforming triangulations.     

Restricted thresholding: recovering smoothness and preserving edges

Restricted non linear approximation is a generalization of the N‐term approximation in which a measure on the index set of the approximants controls the type, instead of the number, of elements in the approximation. Thresholding is a well‐known type of non linear approximation. We relate a generalized upper and lower Temlyakov property with the decreasing rate of the thresholding approximation. This relation is in the form of a characterization through some general discrete Lorentz spaces. Thus, not only we recover some results in the literature but find new ones. As an application of these results, we compress and reduce noise of some images with wavelets and shearlets and show, at least empirically, that the L²‐norm is not necessarily the best norm to measure the approximation error.     

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.     

由Fourier系数确定的函数类的最佳m-项单边逼近渐近估计

结合最佳m项逼近和单边逼近的思想引进所谓最佳m项单边逼近的概念,给出由Fourier系数确定的光滑函数类通过三角函数系在Lp(1≤P≤∞)的最佳m-项单边逼近渐近估计以及m-项类贪婪单边逼近结果.