关于半鞅的可料表示性

利用鞅空间H1的泛函表示定理、泛函分析中的Hahn-Banach定理、半鞅向量随机积分的Girsanov定理,获得了半鞅可料表示性的特征。由于使用的是半鞅的向量随机积分,它推广了经典的结论。     

关于半鞅向量随机积分的两个结果

首先利用半鞅Girsanov定理与闭图像定理证明了:若{Xn}是带滤基的完备概率空间(Ω,F,F,P)中的一列半鞅,其中滤基F=(Ft)t≥0满足通常条件,且{Xn}在关于P的Emery拓扑空间中收敛于X,则当概率测度Q相似文献   

特殊半鞅的强大数定律

利用特殊半鞅的收敛定理和一般形式的Kronecker引理,给出了特殊半鞅和非负特殊半鞅强大数定律的一般形式,推广和完善了已有的结果.     

控制不等式的改进

讨论了随机过程控制关系产生的不等式,对这个不等式给出了一个新系数,改进了以前的结果,并将其应用于局部平方可积鞅.     

Loeb 空间上的拟鞅提升及分解定理

本文通过非标准随机分析方法得到了标准拟鞅提升定理,并构造了非标准拟鞅的分解定理,然后再通过下推方法得到了标准拟鞅的一个分解定理。     

N—指标Poisson过程的鞅刻画

本文给出了N—指标Poisson过程的鞅刻画,并讨论了这种过程的强Markov性.N—指标随机过程(Pt)t∈R_ ~N为Poisson过程的充要条件是(Pt—λmultiply from i=1 to N(t_i))t∈R_ ~N为N—指标鞅,其中t=(t_1,t_2,…,t_N).     

Lévy过程驱动的随机非牛顿流的鞅解

研究了Lévy过程驱动的随机非牛顿流动力系统。研究有限维近似问题解的分布在选定的Hilbert空间中的胎紧性,通过Skorohod嵌入定理和鞅表示定理,得到随机非牛顿流鞅解的存在性。     

随机利率情形下幂型期权定价问题

利用随机微分方程，鞅方法及测度变换等方法，讨论了随机利率情形下的幂型期权定价模型，并得到了随机利率情形下的幂型期权定价公式。     

关于集值拟终鞅的若干结果

在 X~*可分的条件下讨论了集值拟终鞅的若干性质,且在此基础上证明了集值拟终鞅在弱收敛意义下的收敛定理,同时给出了集值拟终鞅的 Riesz 分解定理。     

辅助鞅下投资消费资产模型的一些基本性质

针对所给出的有交易费的资产模型,引入了资产折算函数,并利用辅助鞅和凸函数对偶方法,讨论了该模型下折算资产优化的性质.     

具有分布时滞的随机反应扩散细胞神经网络的几乎必然指数稳定性

研究一类具有分布时滞和反应扩散的随机细胞神经网络的稳定性。通过构造Lyapunov泛函,并利用It公式、半鞅收敛定理以及不等式技巧,得到了系统几乎必然指数稳定的充分条件。     

支持向量顺序回归机的线性规划算法

支持向量顺序回归机是标准支持向量分类机的一个推广，它是一个凸的二次规划问题。本文根据l1范数与l2范数等价关系和优化问题的对偶原理，把凸的二次规划转化成线性规划。由此提了支持向量顺序回归机的线性规划算法，进一步用数值实验验证了此算法的可行性和有效性。并与支持向量顺序回归机相比，它的运行时间缩短了，而且误差i不超过支持向量顺序回归机；     

固体推进剂药柱结构分析的非概率凸集合理论模型

为了研究不确定性能参数对固体推进剂药柱结构分析的影响 ,将非概率凸集合理论模型和粘弹性有限元相结合 ,以增量法处理遗传积分 ,利用摄动法预测其响应量区间 ,发展了一种适合药柱特点的不确定性方法。将其和随机结构分析进行对比 ,表明两种方法之间有一定的联系。     

具有集值H-半-伪单调映射的广义向量变分不等式解的存在性

首先推广向量集值半一单调映射到向量集值H-半-伪单调映射,然后利用Kakutani—Fan—Glicksberg不动点定理,研究了具有集值H-半-伪单调映射的广义向量t变分不等式问题,在自反Banach空间中得到了两个存在性结果。     

Pareto-optimality in classical inventory problems

In this paper the inventory problem with backorders both deterministic and stochastic is studied using trade-off analysis in the context of vector optimization theory. The set of Pareto-optimal solutions is geometrically characterized in both the constrained and unconstrained cases. Moreover, a new way of utilizing Pareto-optimality concepts to handle classical inventory problems with backorders is derived. A new analysis of these models is done by means of a trade-off analysis. New solutions are shown, and an error bound for total inventory cost is provided. Other models such as multi-item or stochastic lead-time demand inventory problems are addressed and their Pareto-optimal solution sets are obtained. An example is included showing the additional applicability of this kind of analysis to handle parametric problems. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 83–98, 1998     

A stochastic approximation algorithm to compute bid prices for joint capacity allocation and overbooking over an airline network

In this article, we develop a stochastic approximation algorithm to find good bid price policies for the joint capacity allocation and overbooking problem over an airline network. Our approach is based on visualizing the total expected profit as a function of the bid prices and searching for a good set of bid prices by using the stochastic gradients of the total expected profit function. We show that the total expected profit function that we use is differentiable with respect to the bid prices and derive a simple expression that can be used to compute its stochastic gradients. We show that the iterates of our stochastic approximation algorithm converge to a stationary point of the total expected profit function with probability 1. Our computational experiments indicate that the bid prices computed by our approach perform significantly better than those computed by standard benchmark strategies and the performance of our approach is relatively insensitive to the frequency with which we recompute the bid prices over the planning horizon. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

Statistical decision analysis of stochastic linear programming problems

This paper presents a statistical decision analysis of a one-stage linear programming problem with deterministic constraints and stochastic criterion function. Procedures for obtaining numerical results are given which are applicable to any problem having this general form. We begin by stating the statistical decision problems to be considered, and then discuss the expected value of perfect information and the expected value of sample information. In obtaining these quantities, use is made of the distribution of the optimal value of the linear programming problem with stochastic criterion function, and so we discuss Monte Carlo and numerical integration procedures for estimating the mean of this distribution. The case in which the random criterion vector has a multivariate Normal distribution is discussed separately, and more detailed methods are offered. We discuss dual problems, including some relationships of this work with other work in probabilistic linear programming. An example is given in Appendix A showing application of the methods to a sample problem. In Appendix B we consider the accuracy of a procedure for approximating the expected value of information.     

On the theory of semi-infinite programming and a generalization of the kuhn-tucker saddle point theorem for arbitrary convex functions

We first present a survey on the theory of semi-infinite programming as a generalization of linear programming and convex duality theory. By the pairing of a finite dimensional vector space over an arbitrarily ordered field with a generalized finite sequence space, the major theorems of linear programming are generalized. When applied to Euclidean spaces, semi-infinite programming theory yields a dual theorem associating as dual problems minimization of an arbitrary convex function over an arbitrary convex set in n-space with maximization of a linear function in non-negative variables of a generalized finite sequence space subject to a finite system of linear equations. We then present a new generalization of the Kuhn-Tucker saddle-point equivalence theorem for arbitrary convex functions in n-space where differentiability is no longer assumed.