广义特征值反问题AX＝BXA的分块中心对称解及其最佳逼近

证明了广义特征值反问题AX＝BXA的分块中心对称解恒存在，给出了其解的一般表达式，给出了解集合中与给定矩阵的最佳逼近解的表达式以及求解最佳逼近解的一个数值算法。     

关于一类一阶脉冲微分系统的初值问题

本文的目的是利用单调迭代方法研究一类一阶脉冲微分系统的初值问题的最小最大拟解的存在性及其迭代逼近程序。     

非光滑第二类Fredolm方程Collocation解的迭代—校正方法

本文对于一类具非光滑核第二类Ｆｒｅｄｈｏｌｍ方程的Ｃｏｌｌｏｃａｔｉｏｎ解提出一种迭代一校正方法，使得在计算量增加很少的前提下，成倍提高逼近解精度，并将此方法用于平面角域上边界积分方程，从而给出了其相应微分方程逼近解的高精度算法，此方法还是一种自适应方法。     

带Hilbert核奇异积分方程的Galerkin方法

该文讨论了以下形式的奇异积分方程其中ａ（ｘ），ｂ（ｘ），ｆ（ｘ），（ｘ）∈Ｈ（２π），ｋ（ｘ，ｔ）关于ｘ，ｔ也∈Ｈ（２π）的数值解法．在Ｌ２模下，得出了逼近解的存在性和收敛性；当ｆ（Ｘ），ｋ（ｘ，ｔ）∈Ｈμ２π，μ＞时，逼近解在最大模下一致收敛到精确解；当ｆ（ｐ）（Ｘ），ｋ（ｘ，ｔ）∈Ｈμ２π时，逼近解对精确解的逼近阶．     

非光滑第二类Fredholm方程Collocation解的迭代──校正方法

本文对于一类具非光滑核第二类Ｆｒｅｄｈｏｌｍ方程的Ｃｏｌｌｏｃａｔｉｏｎ解提出一种迭代─校正方法，使得在计算量增加很少的前提下，成倍提高逼近解精度，并将此方法用于平面多角域上边界积分方程，从而给出其相应微分方程逼近解的高精度算法。此方法还是一种自适应方法。     

有限时区最优控制问题的值函数的逼近

对于有限时区最优连续－转换－脉冲控制问题的情函数满足的拟变分不等式，本文给出了其关于时间的离散化逼近系统，以及逼近解和原粘性解之间的估计．     

一类非线性积分,微分方程的逼近解

在不主紧型条件和耗散型条件的情形下，得到了Ｂａｎａｃｈ空间中一类非线性积分，微分方程的逼近解。     

三维Wigner-Poisson方程组的Cauchy问题

考虑三维 Ｗｉｇｎｅｒ－Ｐｏｉｓｓｏｎ方程组的 Ｃａｕｃｈｙ问题，将 ＷＰ问题转化为等价的 Ｓｃｈｒｏｄｉｎｇｅｒ－Ｐｏｉｓｓｏｎ问题．采用有限区域序列上的解的逼近方法，通过对逼近解建立与区域无关的先验估计，证明了 Ｃａｕｃｈｙ问题解的存在性、唯一性和逼近解的收敛性     

Bernstein-Fan插值算子的逼近阶估计及其算法程序

本文研究了具有三角形波基函数的Bernstein-Fan值算子的收敛定理和逼近阶估计，并给出了它的算法程序。     

存在唯一性定理的一个注记

一阶微分方程解的存在唯一性定理及其证明中 ,以常量函数作为初始逼近函数的情形可推广至以任一连续函数作为初始逼近函数的情形     

非正态情形下过程能力指数的近似置信限

文章讨论了当过程不满足正态性假设的情形下过程能力指数的近似置信限,并以Weibull分布为例给出了指数C′_(pm)的近似置信区间的具体形式和假设检验的检验统计量。最后给出了模拟验证。     

A note on interval estimation of the standard deviation of a gamma population with applications to statistical quality control

Traditional process control charts for a measurement standard deviation are based on the assumption of normality, which may not always be valid. Assuming that measurements follow a gamma distribution, we have obtained an approximate distribution of the sample variance, scaled appropriately. This approximate distribution, which happens to be another gamma model, is used to derive an interval estimate of the population standard deviation. Further, the above approximate gamma model for the sample variance can be used to develop a process control chart as demonstrated by a simulated data set.     

Korovkin type theorems and approximate Hermite–Hadamard inequalities

The main results of this paper offer sufficient conditions in order that an approximate lower Hermite–Hadamard type inequality implies an approximate convexity property. The failure of such an implication with constant error term shows that functional error terms should be considered for the inequalities and convexity properties in question. The key for the proof of the main result is a Korovkin type theorem which enables us to deduce the approximate convexity property from the approximate lower Hermite–Hadamard type inequality via an iteration process.     

基于Lanczos双A-正交的一种修正的QMR算法

本文研究了基于Lanczos双正交过程的拟极小残量法(QMR).将QMR算法中的Lanczos双正交过程用Lanczos双A-正交过程代替,利用该算法得到的近似解与最后一个基向量的线性组合来作为新的近似解,使新近似解的残差范数满足一个一维极小化问题,从而得到一种基于Lanczos双A-正交的修正的QMR算法.数值试验表明,对于某些大型线性稀疏方程组,新算法比QMR算法收敛快得多.     

Improving the Accuracy of Chebyshev Tau Method for Nonlinear Differential Problems

The spectral properties convergence of the Tau method allow to obtain good approximate solutions for linear differential problems advantageously. However, for nonlinear differential problems the method may produce ill-conditioned matrices issued from the approximations obtained in the iterations from the linearization process. In this work we introduce a procedure to approximate nonlinear terms in the differential equations and a new way to build the corresponding algebraic problem improving the stability of the overall algorithm. Introducing the linearization coefficients of orthogonal polynomials in the Tau method within the iterative process, we can go further in the degree to approximate the solution of the differential problems, avoiding the consequences of ill-conditioning.     

A refined iterative algorithm based on the block arnoldi process for large unsymmetric eigenproblems

When the matrix in question is unsymmetric, the approximate eigenvectors or Ritz vectors obtained by orthogonal projection methods including Arnoldi's method and the block Arnoldi method cannot be guaranteed to converge in theory even if the corresponding approximate eigenvalues or Ritz values do. In order to circumvent this danger, a new strategy has been proposed by the author for Arnoldi's method. The strategy used is generalized to the block Arnoldi method in this paper. It discards Ritz vectors and instead computes refined approximate eigenvectors by small-sized singular-value decompositions. It is proved that the new strategy can guarantee the convergence of refined approximate eigenvectors if the corresponding Ritz values do. The resulting refined iterative algorithm is realized by the block Arnoldi process. Numerical experiments show that the refined algorithm is much more efficient than the iterative block Arnoldi algorithm.     

An iterative method with error estimators

Iterative methods for the solution of linear systems of equations produce a sequence of approximate solutions. In many applications it is desirable to be able to compute estimates of the norm of the error in the approximate solutions generated and terminate the iterations when the estimates are sufficiently small. This paper presents a new iterative method based on the Lanczos process for the solution of linear systems of equations with a symmetric matrix. The method is designed to allow the computation of estimates of the Euclidean norm of the error in the computed approximate solutions. These estimates are determined by evaluating certain Gauss, anti-Gauss, or Gauss–Radau quadrature rules.     

跳跃扩散型离散算术平均亚式期权的近似价格公式

在标的资产价格遵循跳跃扩散过程条件下 ,研究没有封闭形式解的离散算术平均亚式期权 ,运用二阶 Edgeworth逼近得到离散算术平均亚式期权的近似价格公式 .     

Approximate filter for a two-dimensional nonlinear diffusion observed in a correlated low noise channel

The purpose of this article is to study a nonlinear filtering problem when the signal is a two-dimensional process from which only the second component is noisy and when only its first (and unnoisy) component is observed in a correlated low noise channel. We propose an approximate finite-dimensional filter and we prove that the filtering error converges to zero. The order of magnitude of the error between the approximate filter and the optimal filter, as the observation noise vanishes, is computed.     

The use of approximate factorization in stiff ODE solvers

We consider implicit integration methods for the numerical solution of stiff initial-value problems. In applying such methods, the implicit relations are usually solved by Newton iteration. However, it often happens that in subintervals of the integration interval the problem is nonstiff or mildly stiff with respect to the stepsize. In these nonstiff subintervals, we do not need the (expensive) Newton iteration process. This motivated us to look for an iteration process that converges in mildly stiff situations and is less costly than Newton iteration. The process we have in mind uses modified Newton iteration as the outer iteration process and a linear solver for solving the linear Newton systems as an inner iteration process. This linear solver is based on an approximate factorization of the Newton system matrix by splitting this matrix into its lower and upper triangular part. The purpose of this paper is to combine fixed point iteration, approximate factorization iteration and Newton iteration into one iteration process for use in initial-value problems where the degree of stiffness is changing during the integration.