多维随机向量序列加权和的中心极限定理

本文研究了多维随机向量序列加权和的渐近行为.利用Lindeberg中心极限定理的基本思想,得到了多维随机向量序列加权和的中心极限定理及其收敛速度,为Lindeberg中心极限定理的推广.     

关于B值鞅的收敛性

本对B值鞅差序列,讨论了其非随机足标与随机足标和的收敛速度．使于浩的关于实值独立随机变量的相应结果得到了推广．     

B值随机元序列加权和的强收敛性

本文研究了B值随机元序列加权和的强收敛性.利用Banach空间的几何性质(p型或p阶光滑),在较弱的条件下得到了B值随机元序列加权和的强大数律,这些结果推广和改进了已知的一些文献中相应的结论.     

模糊化的Egoroff定理

在一般模糊测度空间上,针对可测模糊值函数序列给出了几乎处处收敛,几乎一致收敛和伪几乎一致收敛的概念,并在此基础上,进一步研究了这几种收敛的蕴涵关系,从而获得了模糊化的Egoroff定理,使模糊值函数序列的理论得到进一步丰富.     

随机Bernstein多项式的依分布收敛问题

利用随机的Bernstein多项式研究随机逼近问题具有一定的意义.借助弱收敛的概念,从分布函数的角度,讨论了随机Bernstein多项式依分布收敛问题.同时,与依概率收敛结果相比较,以此说明Bernstein多项式序列依分布收敛适用的范围更广.     

关于关联随机变量序列的极限收敛定理

本文给出了两两PQD的随机变量序列的级数收敛及加权部分和的稳定性的两个结果。另给出了相伴(associated)序列加权和的稳定性的一个结果。最后给出了一个级数收敛定理条件不能减弱的反例。     

■-混合序列加权和的收敛性质

本文在■-混合序列下给出加权和重对数律、完全收敛和强收敛的一些充分条件,这些结论简化了[6],[7]中结论的条件,推广了[8]中定理6并改进了[9]中有关结论,同时给出了加权和的Bernstein不等式.     

关于B值随机元序列的强收敛性

利用截尾方法构造几乎处处收敛的指数鞅，用鞅方法与分析方法相结合，研究了B值适应可积随机元序列的局部收敛性及其强大数定理．     

B值鞅差序列和的收敛速度

在随机元的尾概率随机有界的情况下,我们讨论了Ｂ值鞅差序列非随机足标与随机足标部分和的收敛速度,使得实值独立同分布随机变量序列的一些经典结果得到了推广和一般化,并且当０＜ｔ＜１时,我们证明了对任意Ｂ值随机元结论都是成立的．     

B值随机元的和的尾概率及其收敛速度

§1.引言及主要结果 设B是一可分Banach空间,‖·‖表示其上的范数,{X_n}是定义在同一概率空间上的B值随机元序列,S_n=sum from n=1 to n X_k(在本文以下内容中,均使用这一记号,始终不变),{t_n}是单调不减的正实数序列,并且本文的目的是研究概率的收敛速度问题,其中ε为任一给定的正数。     

Rates of convergence for continued fractions of irrational numbers

The dynamics of the Gauss Map suggests a way to compare the convergence to a real number ζ ε(0,l) of a continued fraction and the divergence of the orbit of ζ Of particular interest is the comparison of the rate of convergence to ζ of its simple continued fraction and the rate of divergence by the Gauss Map of the orbit of ζ for all irrational numbers in (0,l). We state and prove sharp inequalities for the convergence of the sequence of rational convergents of an irrational number ζ. We show that the product of the rate of convergence of the continued fraction of ζ and the rate of divergence by the Gauss Map of the orbit of ζ equals 1.     

Stability of the geometric Ekeland variational principle: Convex case

In geometric terms, the Ekeland variational principle says that a lower-bounded proper lower-semicontinuous functionf defined on a Banach spaceX has a point (x ₀,f(x ₀)) in its graph that is maximal in the epigraph off with respect to the cone order determined by the convex coneK _λ = {(x, α) ∈X × ?:λ ∥x∥ ≤ ? α}, where λ is a fixed positive scalar. In this case, we write (x ₀,f(x ₀))∈λ-extf. Here, we investigate the following question: if (x ₀,f(x ₀))∈λ-extf, wheref is a convex function, and if 〈f _n 〉 is a sequence of convex functions convergent tof in some sense, can (x ₀,f(x ₀)) be recovered as a limit of a sequence of points taken from λ-extf _n ? The convergence notions that we consider are the bounded Hausdorff convergence, Mosco convergence, and slice convergence, a new convergence notion that agrees with the Mosco convergence in the reflexive setting, but which, unlike the Mosco convergence, behaves well without reflexivity.     

A new modified one-step smoothing Newton method for solving the general mixed complementarity problem

In last decades, there has been much effort on the solution and the analysis of the mixed complementarity problem (MCP) by reformulating MCP as an unconstrained minimization involving an MCP function. In this paper, we propose a new modified one-step smoothing Newton method for solving general (not necessarily P₀) mixed complementarity problems based on well-known Chen-Harker-Kanzow-Smale smooth function. Under suitable assumptions, global convergence and locally superlinear convergence of the algorithm are established.     

Simultaneous Point Estimates for Newton's Method

Beside the classical Kantorovich theory there exist convergence criteria for the Newton iteration which only involve data at one point, i.e. point estimates. Given a polynomial P, these conditions imply the point evaluation of n = deg(P) functions (from a certain Taylor expansion). Such sufficient conditions ensure quadratic convergence to a single zero and have been used by several authors in the design and analysis of robust, fast and efficient root-finding methods for polynomials.In this paper a sufficient condition for the simultaneous convergence of the one-dimensional Newton iteration for polynomials will be given. The new condition involves only n point evaluations of the Newton correction and the minimum mutual distance of approximations to ensure simultaneous quadratic convergence to the pairwise distinct n roots.     

垂直线性互补问题的一种光滑算法

文中给出了垂直线性互补问题的一个新的光滑价值函数,不同于光滑化方法中的价值函数,它不包含任何必须趋向零的参数,因此算法中不涉及参数调整步骤,而且具有良好的强制性.基此价值函数,提出了求解垂直线性互补问题的一种阻尼Newton类算法,并证明了该算法对竖块P0+R0矩阵的垂直线性互补问题具有全局收敛性;当解满足相当于BD-正则条件时,算法具有局部二次收敛性;在不增加额外校正步骤(算法的每个迭代步只求解一个Newton方程)的情形下,算法对竖块P-矩阵垂直线性互补问题(无须假设严格互补),具有有限步收敛性.数值实验结果令人满意.     

Alternative Approaches to the Two-Scale Convergence

Two-scale convergence is a special weak convergence used in homogenization theory. Besides the original definition by Nguetseng and Allaire two alternative definitions are introduced and compared. They enable us to weaken requirements on the admissibility of test functions (x, y). Properties and examples are added.     

A Non-Interior Continuation Algorithm for the P0 or P* LCP with Strong Global and Local Convergence Properties

We propose a non-interior continuation algorithm for the solution of the linear complementarity problem (LCP) with a P₀ matrix. The proposed algorithm differentiates itself from the current continuation algorithms by combining good global convergence properties with good local convergence properties under unified conditions. Specifically, it is shown that the proposed algorithm is globally convergent under an assumption which may be satisfied even if the solution set of the LCP is unbounded. Moreover, the algorithm is globally linearly and locally superlinearly convergent under a nonsingularity assumption. If the matrix in the LCP is a P_* matrix, then the above results can be strengthened to include global linear and local quadratic convergence under a strict complementary condition without the nonsingularity assumption.     

A certain class of weighted statistical convergence and associated Korovkin‐type approximation theorems involving trigonometric functions

The subject of statistical convergence has attracted a remarkably large number of researchers due mainly to the fact that it is more general than the well‐established theory of the ordinary (classical) convergence. In the year 2013, Edely et al 17 introduced and studied the notion of weighted statistical convergence. In our present investigation, we make use of the (presumably new) notion of the deferred weighted statistical convergence to present Korovkin‐type approximation theorems associated with the periodic functions , and defined on a Banach space . In particular, we apply our concept of the deferred weighted statistical convergence with a view to proving a Korovkin‐type approximation theorem for periodic functions and also to demonstrate that our result is a nontrivial extension of several known Korovkin‐type approximation theorems which were given in earlier works. Moreover, we establish another result for the rate of the deferred weighted statistical convergence for the same set of functions. Finally, we consider a number of interesting special cases and illustrative examples in support of our definitions and of the results which are presented in this paper.     

Convergence of interval-type algorithms for generalized fractional programming

The purpose of this paper is to analyze the convergence of interval-type algorithms for solving the generalized fractional program. They are characterized by an interval [LB _k , UB _k ] including*, and the length of the interval is reduced at each iteration. A closer analysis of the bounds LB _k and UB _k allows to modify slightly the best known interval-type algorithm NEWMODM accordingly to prove its convergence and derive convergence rates similar to those for a Dinkelbach-type algorithm MAXMODM under the same conditions. Numerical results in the linear case indicate that the modifications to get convergence results are not obtained at the expense of the numerical efficiency since the modified version BFII is as efficient as NEWMODM and more efficient than MAXMODM.This research was supported by NSERC (Grant A8312) and FCAR (Grant 0899).     

关于A-收敛

设A={ai}（i=1）∞S_（e_1）~＋,其中S（e1）＋={x=（x（n））∈e1：‖x‖=1且x（n）≥0对任意的n∈N}.Banach空间X中的序列{x_n}称为A-收敛于x∈X是指对任意的ε〉0,→0当i→∞,其中A（ε）={n∈N：‖x_n-x‖≥ε}.这篇文章中,我们证明了该收敛可以用一个有限可加的概率测度加以刻画.我们对A-收敛与统计收敛的关系进行了讨论,证明了A-收敛为统计收敛完全取决于A的w~＊-拓扑性质.