集值Pramart的一类鞅逼近

在X^＊可分的条件下,首先讨论了集值Pramart有关支撑函数和距离函数的性质,利用支撑函数和距离函数研究了集值Pramart鞅逼近,在此基础上,给出了集值Pramart的一类鞅分解.     

集值L^1—极限鞅的集值鞅逼近及其收敛性

本文证明了集值Ｌ~１－极限鞅的集值鞅逼近定理,并利用此结果以及集值鞅的收敛性结果讨论了集值Ｌ~１－极限鞅的收敛性．     

集值拟鞅和集值一致渐近鞅

本文讨论了集值拟鞅和集值一致渐近鞅,证明了集值拟鞅与集值一致渐近鞅的选样定理,对于集值一致渐近鞅得到了一些收敛性结果,并由此刻化了空间的 Radon-Nikodym性质.     

集值下(上)鞅的 Doob 分解

本文研究集值下(上)鞅的 Doob 分解.我们得到一些确保 Doob 分解存在的充要条件,并给出例子说明并非所有集值下(上)鞅都有 Doob 分解.     

集值上鞅的轨道正则性

本文证明了在有限维Ｂａｎａｃｈ空间中，任一闭凸集值鞅（在ｋ－收敛意义下）存在右连左极修正．     

一种反向集值鞅型序列的收敛性

本文研究了在非空有界闭凸集的上鞅收敛性和有界闭凸集的适应可积集值序列的收敛性.引入了反向集值mil的基础上,得到了收敛性定理及定理证明.     

集值序上鞅

本文研究了连续时间的集值序上鞅．在一定的条件下首先证明了集值序上鞅有（Ｋ－Ｍ）Ｒｉｅｓｚ分解;然后证明了集值序上鞅的Ｄｏｏｂ－停时定理．     

集值上鞅的收敛定理及 Riesz 分解

本文给出了集值鞅的进一步性质;建立了集值上鞅外穿不等式;证明了一个集值上鞅收敛定理;研究了集值上鞅的 Riesz 分解.     

闭区间值鞅及模糊数值鞅

集值上鞅、下鞅和鞅的收敛定理已有不少文章进行了研究[1]、[2][3]。但在这些文章中,集值上(下)鞅并不以经典的上(下)鞅为其特款。在本文中,我们定义了以经典上(下)鞅为其特款的闭区间值上(下)鞅,并讨论了它们的性质及其收敛定理。本文还在此基础上讨论了模糊数值鞅。     

集值逆上鞅的收敛定理及应用

研究了取值于Ｂａｎａｃｈ空间的集值逆上鞅的收敛性，给出了集值逆上鞅在集列Ｗｉｊｓｍａｎ收敛，弱收敛及Ｋｕｒａｔｏｗｓｋｉ－Ｍｏｓｃｏ收敛意义下的收敛定理，并给出了它们在连续参数集值鞅中的应用。     

集值上鞅可Doob分解的条件

假定(X,‖·‖)为实Banach空间,X*为其对偶空间,X*可分.给出了集值上鞅几种不同的Doob分解概念,利用支撑函数研究了集值上鞅在各种分解意义下可Doob分解的充分必要条件.     

集值测度的Radon-Nikodym定理及集值算子的Pettis-Aumann积分表示

本文建立了 Ｂａｎａｃｈ空间集值测度的 Ｒａｄｏｎ－Ｎｉｋｏｄｙｍ定理,并给出了两类集值算子的Ｐｅｔｔｉｓ－Ａｕｍａｎｎ积分表示．     

Set-Valued Stochastic Integrals and Equations with Respect to Two-Parameter Martingales

This article is concerned with notions of set-valued stochastic integrals driven by two-parameter martingales and increasing processes. We investigate their main properties and we consider next multivalued stochastic integral equations in the plane. We establish the existence and uniqueness of solutions to such equations as well as their additional properties.     

广义集值强非线性混合似变分不等式解的迭代逼近

辅助原理的技巧被延拓来研究一类取非紧值的集值映象的广义强非线性混合似变分不等式.首先,证明了这类广义强非线性混合似变分不等式的辅助问题解的存在性.其次,利用该存在性结果,给出了解这类广义强非线性混合似变分不等式的迭代算法.最后,不仅证明了这类广义强非线性混合似变分不等式解的存在性,而且证明了由算法生成的迭代序列的收敛性.     

A New Variant of Ekeland's Variational Principle for Set-Valued Maps

In this article, following the idea used by Göpfert et al . [A. Göpfert, Chr. Tammer and C. Zalinescu (2000). On the vectorial Ekeland's variational principle and minimal points in product spaces. Nonlinear Analysis, Theory, Methods & Applications , 39 , 909-922] to derive an Ekeland's variational principle for vector-valued functions, we derive a new variant of Ekeland's variational principle for set-valued maps. Finally, we apply this variational principle to obtain an approximate necessary optimality condition for a class of set-valued optimization problems.     

On Set-Valued Stochastic Integrals

We define a set-valued stochastic integral with respect to a 1-dimensional Brownian motion. The paper develops multivalued analogs to the theory of singlevalued stochastic integrals. It is expected that these results will be useful to study set-valued and fuzzy stochastic analysis.     

General Ekeland's Variational Principle for Set-Valued Mappings

In this paper, we introduce the concept of approximate solutions for set-valued optimization problems. A sufficient condition for the existence of approximate solutions is obtained. A general Ekeland's variational principle for set-valued mappings in complete ordered metric spaces and complete metric spaces are derived. These results are generalizations of results for vector-valued functions in Refs. 1–4.     

Minimax Problems for Set-Valued Mappings

In this article, by virtue of the Fan-Browder fixed point theorem, we first obtain a minimax theorem and establish an equivalent relationship between the minimax theorem and a cone saddle point theorem for scalar set-valued mappings. Then, by using scalarization functions, we investigate an existence theorem of the cone saddle point for set-valued mappings. Simultaneously, we also establish a minimax theorem for set-valued mappings.     

向量集值映射的共轭对偶

借助抽象算子将共轭映射的概念的到抽象空间,引入了集值映射的共轭映射和次梯度,据此讨论了集值映射共轭对偶的全局稳定性。     

First and Second-Order Lagrange Claims for Set-Valued Maps

In this note, we derive first and second-order necessary (resp. sufficient) conditions for local minimum (resp. strict local minimum) points of optimization problems governed by set-valued maps. This allows us to present and to solve a generalized Lagrange claim in a multivalued setting.