由Lvy过程驱动的BSDE定义的一般非线性期望

本文研究了平凡可积随机变量的一类非线性期望f-期望.利用陈增敬推广g-期望的方法,扩张了f-期望的定义空间.     

Lévy过程驱动的BSDE的反比较定理与Jensen不等式（英文）

本文研究了由一维Lévy过程驱动的倒向随机微分方程(BSDE)的反比较定理.利用一般g-期望下BSDE的反比较定理的证明方法,推导出了一般f-期望下BSDE的反比较定理,并给出了一般f-期望下Jensen不等式成立的充分必要条件.     

关于扭曲数学期望的几个性质

本文研究了扭曲数学期望. 利用构造示性函数的方法, 证明了扭曲数学期望满足常数平移不变性的充要条件是该扭曲数学期望是熵期望.     

关于扭曲数学期望的几个性质

本文研究了扭曲数学期望.利用构造示性函数的方法,证明了扭曲数学期望满足常数平移不变性的充要条件是该扭曲数学期望是熵期望.     

一类f鞅的极大值不等式(英文)

本文研究了f框架下有关f-鞅的不等式性质.利用构造停时的方法和带跳的倒向随机微分方程的比较定理,得到了一类f-鞅的极大值不等式,推广了经典鞅论中的极大值不等式.     

一般的非线性数学期望—g—期望

彭实戈利用倒向随机微分方程引入了平方可积随机变量的非线性数学期望－ｇ－期望,本文扩张了ｇ－期望的定义空间。     

报童问题及其商业拓展的两类利润期望模型

基于会计利润与经济利润的不同意义和经济利润在经济管理中的重要性,明确区分并解释了离散型随机需求量报童问题及其商业拓展的这两类利润期望,得出了随机需求量取值个数有限的报童问题及其商业拓展的会计利润期望模型和经济利润期望模型及其利润期望最大化进货量的计算关系式;着重区分了相应的显性成本期望和隐性成本期望;指出求解此类问题运筹学的处理方法与经济学原理并不相悖.     

L~p空间中随机变量的g-期望与条件g-期望

利用倒向随机微分方程的Lp解定义了Lp空间中随机变量g-期望与条件g-期望,扩张了g-期望与条件g-期望的定义空间;证明了用Lp解定义的g-期望与文[5]用算子连续扩张方法定义的一般g-期望的一致性,得到了Lp空间中随机变量的g-期望与条件g-期望的一些性质.     

Lp空间中随机变量的g-期望与条件g-期望

利用倒向随机微分方程的Lp解定义了Lp空间中随机变量g-期望与条件g-期望,扩张了g-期望与条件g-期望的定义空间;证明了用Lp解定义的g-期望与文[5]用算子连续扩张方法定义的一般g-期望的一致性,得到了Lp空间中随机变量的g-期望与条件g-期望的一些性质.     

Choquet期望,最大(最小)期望和推广的Peng’s g-期望（英文）

Choquet期望和最大(最小)期望是非线性期望,它们替代经典的数学期望被广泛地应用在经济、金融和保险中.但是,由于非线性,计算它们往往非常困难.本文首先介绍推广的Peng’s g-期望及其相关性质;然后,给出最大(最小)期望和推广的Peng’s g-期望之间的关系;最后,利用Peng’s g-期望,在一些合理假设下,得到Choquet期望和最大(最小)期望是一致的.     

Jensen’s Inequality Under Nonlinear Expectation Generated by BSDE with Jumps

Acta Mathematicae Applicatae Sinica, English Series - In this paper, we study Jensen’s inequality under f-expectation, which is a nonlinear expectation generated by backward stochastic...     

Optimal control of nonlinear evolution inclusions

In this paper, we study the optimal control of nonlinear evolution inclusions. First, we prove the existence of admissible trajectories and then we show that the set that they form is relatively sequentially compact and in certain cases sequentially compact in an appropriate function space. Then, with the help of a convexity hypothesis and using Cesari's approach, we solve a general Lagrange optimal control problem. After that, we drop the convexity hypothesis and pass to the relaxed system, for which we prove the existence of optimal controls, we show that it has a value equal to that of the original one, and also we prove that the original trajectories are dense in an appropriate topology to the relaxed ones. Finally, we present an example of a nonlinear parabolic optimal control that illustrates the applicability of our results.This research was supported by NSF Grant No. DMS-88-02688.     

Hereditary atomicity in integral domains

If every subring of an integral domain is atomic, we say that the latter is hereditarily atomic. In this paper, we study hereditarily atomic domains. First, we characterize when certain direct limits of Dedekind domains are Dedekind domains in terms of atomic overrings. Then we use this characterization to determine the fields that are hereditarily atomic. On the other hand, we investigate hereditary atomicity in the context of rings of polynomials and rings of Laurent polynomials, characterizing the fields and rings whose rings of polynomials and rings of Laurent polynomials, respectively, are hereditarily atomic. As a result, we obtain two classes of hereditarily atomic domains that cannot be embedded into any hereditarily atomic field. By contrast, we show that rings of power series are never hereditarily atomic. Finally, we make some progress on the still open question of whether every subring of a hereditarily atomic domain satisfies ACCP.     

具有伪单调算子的非线性微分包含约束的最优控制

本文研究一类受主算子为伪单调算子的非线性微分包含约束的最优控制问题．首先,探讨抛物型发展方程的柯西问题其解的性质及微分包含问题的容许轨线的存在性;然后,利用一个新的可测选择定理解决了受非线性微分包含约束的最优控制的存在性,最后,给一例子加以说明所获结果的应用性．     

极值分布的一个注记

本文对PH极值分布进行了推广,应用构造相关联的Markov过程的方法,证明了n个相互独立的PH随机变量构成的次序随机变量的分布仍是PH分布。并给出了次序PH随机变量分布表达式的表示方法,本文同时也给出了次序PH随机变量的联合生存分布,本文最后给出了次序PH随机变量在可靠性理论与更新理论中的应用。     

On a constructive approximation of the efficient outcomes in bicriterion vector optimization

For bicriterion quasiconvex optimization problems, we present a constructive procedure for an approximation of the efficient outcomes. Performing this procedure we can estimate the accuracy of the approximation. Conversely, if we prescribe an accuracy for the approximation, we can calculate the number of points which have to be computed by a certain scalarization method to remain under the given accuracy. Finally, we give a numerical example.     

Expanding topological space,study and applications

In this paper, we introduce the notion of expanding topological space. We define the topological expansion of a topological space via local multi-homeomorphism over coproduct topology, and we prove that the coproduct family associated to any fractal family of topological spaces is expanding. In particular, we prove that the more a topological space expands, the finer the topology of its indexed states is. Using multi-homeomorphisms over associated coproduct topological spaces, we define a locally expandable topological space and we prove that a locally expandable topological space has a topological expansion. Specifically, we prove that the fractal manifold is locally expandable and has a topological expansion.     

基于粗糙集与模糊集的连续值域决策表的离散化算法

将粗糙集理论与模糊集理论结合起来,给出一种连续值域决策表的离散化算法。该算法从已知数据的初始决策系统出发,首先构造对像的相似矩阵,然后根据相似矩阵的传递闭包及粗糙集正域的思想得出决策表的条件类,再根据条件类将连续值决策表化为区间值决策表,最后根据各区间值将连续值域决策表化为离散决策表。     

The value of foresight

Suppose you have one unit of stock, currently worth 1, which you must sell before time $T$. The Optional Sampling Theorem tells us that whatever stopping time we choose to sell, the expected discounted value we get when we sell will be 1. Suppose however that we are able to see $a$ units of time into the future, and base our stopping rule on that; we should be able to do better than expected value 1. But how much better can we do? And how would we exploit the additional information? The optimal solution to this problem will never be found, but in this paper we establish remarkably close bounds on the value of the problem, and we derive a fairly simple exercise rule that manages to extract most of the value of foresight.