s-乘数收敛及Orlicz-Pettis型定理

本文给出了在局部凸空间中与弱拓扑具有相同的ｓ－乘数收敛点列的最强的可允许极拓扑Ｆ（μ_ｓ）的刻划．并给出Ｆ（μｓ）＝β（Ｘ,Ｘ＇）的充分条件和必要条件,由此证明了ｃ０（或ｌｐ,０＜ｐ＜∞）－乘数收敛性是对可允许极拓扑全体而言的不变性,     

抽象对偶对上的滑脊性(英文)

给出F-弱滑脊性的定义,利用此性质,证明如果λ是一个具有F-弱滑脊性的数量空间,λ-乘数无序收敛是一个对偶不变性.如果(λ,β(λ,λ~(uβ)))是FAK-空间,则上述性质变成全程不变性.     

关于λ-数乘收敛级数的不变性

本文证得（1）若λ具有弱滑脊性，那么λ-数乘收敛级数具有对偶不变性。（2）若λ包含C00，那么λ-数乘收敛级数具有全程不变性的充要条件为（λ，β（λ，λβ））是AK－空间。     

真商群为s-自对偶群的有限p-群

如果有限群G的每个子群与G的某个商群同构,则称群G为s-自对偶群.如果s-自对偶群G的每个商群与G的某个子群同构,则称群G为自对偶群.本文分类了每个真商群均为s-自对偶群的有限p-群.作为推论,本文还分类了每个真截段均为s-自对偶群的有限p-群,每个真商群均为自对偶群的有限p-群,以及每个真截段均为自对偶群的有限p-群.     

一类具有时滞的Watt型恒化器模型的稳定性分析

基于"比例依赖"理论,研究了一类具有时滞和Watt型功能反应函数的恒化器模型.详细讨论了正平衡点的局部渐近稳定性,证明了系统在特定的时滞参量值下将产生Hopf分支.利用Lyapunov-LaSalle不变性原理,得到了正平衡点全局渐近稳定的充分条件.     

非凸分式优化问题的最优性充分条件和对偶

通过引入广义弧连通概念,在Rn空间中,研究极大极小非凸分式规划问题的最优性充分条件及其对偶问题.首先获得了极大极小非凸分式规划问题的最优性充分条件;然后建立分式规划问题的一个对偶模型并得到了弱对偶定理,强对偶定理和逆对偶定理.     

g-期望的分布不变性

当g-期望具有分布不变性时,给出了g所满足的充要条件.并证明了当且仅当g为零时,g-期望其有分布不变性.     

一类非光滑规划问题的最优性和对偶

研究一类非光滑多目标规划问题,给出了该规划问题的三个最优性充分条件.同时,研究了该问题的对偶问题,给出了相应的弱对偶定理和强对偶定理.     

约束Birkhoff系统的形式不变性

约束Birkhoff系统的形式不变性是约束Birkhoff方程在无限小变换下的一种不变性。给出约束Birkhoff系统形式不变性的定义与判据,并研究了这种形式不变性与Noether对称性之间的关系。     

一类非光滑优化问题的最优性与对偶

本文研究了一类带等式和不等式约束的非光滑多目标优化问题,给出了该类问题的Karush-Kuhn-Tucker最优性必要条件和充分条件,建立了该类规划问题的一类混合对偶模型的弱对偶定理、强对偶定理、逆对偶定理、严格逆对偶定理和限制逆对偶定理.     

Hölderian Invariance Principle for Triangular Arrays of Random Variables

In this paper, we extend the Hölderian invariance principle of Lamperti [6] to the case of partial-sum processes based on a triangular array of row-wise independent random variables. As an application, we obtain necessary and sufficient conditions for the almost sure (resp. in probability) weak Hölder convergence of partial-sum processes based on bootstrapped samples.     

强不变原理与完全收敛性的统一形式

本文建立了强不变原理与完全收效性的统一形式，得到了用强不变原理研究完全收敛性的方法，前人许多关于强不交原理和完全收敛性的结论是本文的推论．     

Uniform CLT for Markov chains and its invariance principle: A martingale approach

The convergence of stochastic processes indexed by parameters which are elements of a metric space is investigated in the context of an invariance principle of the uniform central limit theorem (UCLT) for stationary Markov chains. We assume the integrability condition on metric entropy with bracketing. An eventual uniform equicontinuity result is developed which essentially gives the invariance principle of the UCLT. We translate the problem into that of a martingale difference sequence as in Gordin and Lifsic.⁽⁷⁾ Then we use the chaining argument with stratification adapted from that of Ossiander.⁽¹¹⁾ The results of this paper generalize those of Levental⁽¹⁰⁾ and Ossiander.⁽¹¹⁾     

On the invariance principle for differentiable statistical functionals

Sup-norm differentiability of a statistical functional T is shown to be a sufficient condition for invariance principles for T. Application of the result to repeated significance testing is sketched.     

Empirical processes indexed by smooth functions

In this paper we derive a general invariance principle for empirical processes indexed by smooth functions. The method is applied to prove bounds for the convergence of the empirical distributions which might be useful to verify asymptotic normality of smooth statistical functionals. As one further application we get the convergence of the so-called empirical characteristic function process.     

Stochastic approximation with dependent noise

In this work we derive the usual limit laws (weak and strong convergence, central limit theorem, invariance principle) for stochastic approximation with stationary noise. The idea is to introduce an artificial sequence, related to the SA scheme, but which clearly obeys the desired limit law. This sequence is subtracted from the SA scheme and the remainder, which behaves more or less deterministically, is shown to vanish using simple limit arguments.     

A Geometric Characterization of Viable Sets for Controlled Degenerate Diffusions

For a general controlled diffusion process and an arbitrary closed set K we study the viability, or weak invariance, or controlled invariance, of K, that is, the existence of a control for each initial point in K keeping the trajectory forever in K. By viscosity solutions methods we prove a simple necessary and sufficient condition involving only a deterministic second-order normal cone to K and the data of the diffusion process. We also give an extension to stochastic differential games.     

A non-standard-Euler–Maruyama scheme

We construct a non-standard finite difference numerical scheme to approximate stochastic differential equations (SDEs) using the idea of weighed step introduced by R.E. Mickens. We prove the strong convergence of our scheme under locally Lipschitz conditions of a SDE and linear growth condition. We prove the preservation of domain invariance by our scheme under a minimal condition depending on a discretization parameter and unconditionally for the expectation of the approximate solution. The results are illustrated through the geometric Brownian motion. The new scheme shows a greater behaviour compared with the Euler–Maruyama scheme and balanced implicit methods which are widely used in the literature and applications.     

Dual Convergence for Penalty Algorithms in Convex Programming

Algorithms for convex programming, based on penalty methods, can be designed to have good primal convergence properties even without uniqueness of optimal solutions. Taking primal convergence for granted, in this paper we investigate the asymptotic behavior of an appropriate dual sequence obtained directly from primal iterates. First, under mild hypotheses, which include the standard Slater condition but neither strict complementarity nor second-order conditions, we show that this dual sequence is bounded and also, each cluster point belongs to the set of Karush–Kuhn–Tucker multipliers. Then we identify a general condition on the behavior of the generated primal objective values that ensures the full convergence of the dual sequence to a specific multiplier. This dual limit depends only on the particular penalty scheme used by the algorithm. Finally, we apply this approach to prove the first general dual convergence result of this kind for penalty-proximal algorithms in a nonlinear setting.