有界可料过程关于集值平方可积鞅的集值随机积分

本文定义了一类有界可料过程关于集值平方可积鞅的集值随机积分,并研究了集植随机积分的性质。此为建立集值随机分析的理论奠定了基础。     

实值非负函数关于集值序增函数的集值Riemann-Stieltjes积分

本文首先建立了实值非负函数关于集值序增函数的集值Riemann-Stieltjes积分,并讨论了集值Riemann-Stieltjes积分的性质,给出了集值Riemann-Stieltjes可积的充要条件,最后引入了集值Riemann-Stieltjes随机积分．     

自反B空间中集值增过程的对偶投影

假定Ａ是以自反Ｂａｎａｃｈ空间中弱紧凸集为值的集值增过程，本文研究了非负有界可测过程关于Ａ的积分以及Ａ在乘积可测空间上生成的集值测度，证明了每个可积集值增过程存在唯一对偶可选（可料）投影．     

有界闭凸值测度的集值积分

引入实值函数关于有界闭凸值测度的集值积分,并讨论了集值积分的收敛定理,证明了当集值测度为有界闭凸集值的有界变差集值测度时,关于弱紧凸集值测度的积分性质对有界闭凸集值测度仍然保持.推广了实值函数关于弱紧凸值测度的积分.     

关于模糊集值平方可积鞅的随机积分

给出了模糊集值平方可积鞅的定义以及简单实值可料过程关于模糊集值平方可积鞅的随机积分的定义;证明了该积分仍具有模糊集值平方可积鞅的性质。     

关于模糊随机过程对标准Wiener过程的It(o)积分

提出了关于模糊随机过程的标准维纳过程的伊藤积分的定义和性质,证明了变上限伊藤积分是一个几乎处处连续平方模糊可积鞅.     

一类集值有界变差过程及其对偶投影

本文给出了一类集值有界变差过程的定义,并在有限维情况下证明了集值有界变差过程的可选(可料)对偶投影的存在唯一性。     

集值随机过程的投影

本文研究了集值可积变差随机过程的可选和可料对偶投影.当Banach空间X具有RNP,其对偶空间X*可分时,证明了Pwkc(X)值的可积变差过程存在唯一的可选和可料对偶投影.最后讨论了集值随机过程对偶投影的性质.     

自反B空间中可测集值随机过程的可选（可料）投影

设Ｘ是可分自反Ｂａｎａｃｈ空间，（Ｆ，Ａ，ｔ∈Ｒ＋）是取值于Ｘ中闭凸集的可测集值随机过程，该文证明了，若任给停时Ｔ，ＦＴＩ（Ｔ〈∞）关于ＡＴ（相应地关于Ａ）是σ一可积，则（Ｆ，ｔ∈Ｒ＋）必存在可选（相应地可料）投影过程。     

集值随机过程的投影

本文研究了集值可积变差随机过程的可选和可料对偶投影．当Ｂａｎａｃｈ空间Ｘ具有ＲＮＰ,其对偶空间Ｘ＊可分时,证明了Ｐｗｋｃ（Ｘ）值的可积变差过程存在唯一的可选和可料对偶投影．最后讨论了集值随机过程对偶投影的性质．     

Multiple stochastic integrals constructed by special expansions of products of the integrating stochastic processes

The paper deals with problems of constructing multiple stochastic integrals in the case when the product of increments of the integrating stochastic process admits an expansion as a finite sum of series with random coefficients. This expansion was obtained for a sufficiently wide class including centered Gaussian processes. In the paper, some necessary and sufficient conditions are obtained for the existence of multiple stochastic integrals defined by an expansion of the product of Wiener processes. It was obtained a recurrent representation for the Wiener stochastic integral as an analog of the Hu–Meyer formula.     

Stochastic Invariance for Differential Inclusions

The aim of this paper is to combine two ways for representing uncertainty through stochastic differential inclusions: a 'stochastic uncertainty", driven by a Wiener process, and a 'contingent uncertainty", driven by a set-valued map. The paper is also devoted to the invariance of closed under stochastic differential inclusions with a Lipschitz right-hand side, characterized in terms of stochastic tangent sets to closed subsets.     

A weak stochastic integral in Banach space with application to a linear stochastic differential equation

Cylindrical Wiener processes in real separable Banach spaces are defined, and an approximation theorem involving scalar Wiener processes is given for such processes. A weak stochastic integral for Banach spaces involving a cylindrical Wiener process as integrator and an operator-valued stochastic process as integrand is defined. Basic properties of this integral are stated and proved.A class of linear, time-invariant, stochastic differential equations in real, separable, reflexive Banach spaces is formulated in such fashion that a solution of the equation is a cylindrical process. An existence and uniqueness theorem is proved. A stochastic version of the problem of heat conduction in a ring provides an example.Research supported by National Science Foundation under Grant No. ECS-8005960.     

Ornstein–Uhlenbeck processes in Hilbert space with non-Gaussian stochastic volatility

We propose a non-Gaussian operator-valued extension of the Barndorff-Nielsen and Shephard stochastic volatility dynamics, defined as the square-root of an operator-valued Ornstein–Uhlenbeck process with Lévy noise and bounded drift. We derive conditions for the positive definiteness of the Ornstein–Uhlenbeck process, where in particular we must restrict to operator-valued Lévy processes with “non-decreasing paths”. It turns out that the volatility model allows for an explicit calculation of its characteristic function, showing an affine structure. We introduce another Hilbert space-valued Ornstein–Uhlenbeck process with Wiener noise perturbed by this class of stochastic volatility dynamics. Under a strong commutativity condition between the covariance operator of the Wiener process and the stochastic volatility, we can derive an analytical expression for the characteristic functional of the Ornstein–Uhlenbeck process perturbed by stochastic volatility if the noises are independent. The case of operator-valued compound Poisson processes as driving noise in the volatility is discussed as a particular example of interest. We apply our results to futures prices in commodity markets, where we discuss our proposed stochastic volatility model in light of ambit fields.     

On the Convergence of the P-Algorithm for One-Dimensional Global Optimization of Smooth Functions

The Wiener process is a widely used statistical model for stochastic global optimization. One of the first optimization algorithms based on a statistical model, the so-called P-algorithm, was based on the Wiener process. Despite many advantages, this process does not give a realistic model for many optimization problems, particularly from the point of view of local behavior. In the present paper, a version of the P-algorithm is constructed based on a stochastic process with smooth sampling functions. It is shown that, in such a case, the algorithm has a better convergence rate than in the case of the Wiener process. A similar convergence rate is proved for a combination of the Wiener model-based P-algorithm with quadratic fit-based local search.     

An extension of the Wong-Zakai theorem for stochastic evolution equations in Hilbert spaces

In this paper we examine an approximation theorem of the Wong–Zakai type for stochastic evolution equations in a Hilbert space with the noise being the generalized derivative of the Wiener process with values in another Hilbert space. As a consequence of the approximation of the Wiener process we get in the limit equation the Ito correction term for the infinite dimensional case. The obtained result includes the case of stochastic delay equations. The uniqueness and existence of solutions are guaranteed by known theorems for the mild solutions     

A Deterministic Approach To Stochastic Optimal Control With Application To Anticipative Control

Using the decomposition of solution of SDE, we consider the stochastic optimal control problem with anticipative controls as a family of deterministic control problems parametrized by the paths of the driving Wiener process and of a newly introduced Lagrange multiplier stochastic process (nonanticipativity equality constraint). It is shown that the value function of these problems is the unique global solution of a robust equation (random partial differential equation) associated to a linear backward Hamilton-Jacobi-Bellman stochastic partial differential equation (HJB SPDE). This appears as limiting SPDE for a sequence of random HJB PDE's when linear interpolation approximation of the Wiener process is used. Our approach extends the Wong-Zakai type results [20] from SDE to the stochastic dynamic programming equation by showing how this arises as average of the limit of a sequence of deterministic dynamic programming equations. The stochastic characteristics method of Kunita [13] is used to represent the value function. By choosing the Lagrange multiplier equal to its nonanticipative constraint value the usual stochastic (nonanticipative) optimal control and optimal cost are recovered. This suggests a method for solving the anticipative control problems by almost sure deterministic optimal control. We obtain a PDE for the “cost of perfect information” the difference between the cost function of the nonanticipative control problem and the cost of the anticipative problem which satisfies a nonlinear backward HJB SPDE. Poisson bracket conditions are found ensuring this has a global solution. The cost of perfect information is shown to be zero when a Lagrangian submanifold is invariant for the stochastic characteristics. The LQG problem and a nonlinear anticipative control problem are considered as examples in this framework     

Dynamics of stochastic partly dissipative systems on time-varying domain

This article is devoted to stochastic partly dissipative systems (SPDS) defined on time-varying domain expending in time. The definition of a space–time trace class Wiener process on time-varying domain is presented by combining a time trace class Wiener process with a spatial intensity defined on time-varying domain. Some nice properties for Wiener process on time-varying domain are also presented. We develop a new penalty method to establish the existence and uniqueness of variational solution of SPDS with additive noise on time-varying domain. The existence of global attractor for the process generated by variational solution is also obtained.     

Use of stochastic control theory to model a forest management system

A forest management problem due to Hellman has been modelled as a stochastic control problem with one state variable (inventory level) and one control variable (consumption rate of wood by the factories). The stochastic process governing the evolution of the inventory level is transformed into an Itô stoachastic differential equation by approximating the compound Poisson process of wood arrivals into the depot as a Wiener process. The resulting stochastic control problem is solved by using the Hamilton-Jacobi-Bellman equation of stochastic dynamic programming. Two numerical examples illustrate the results.     

拟必然分析概述

经典的几乎必然分析研究的是随机变量和或机过程除去概率为零的集合以外的性质，容度的概念起源于电学，其严格的数学理论由Ｎ．Ｗｉｅｎｅｒ及Ｇ．Ｃｈｏｑｕｅｔ所发展，８０年代Ｐ．Ｍａｌｌｉａｖｉｎ建立了Ｗｉｅｎｅｒ空间上的非线性容度理论，拟必然分析便是研究除去零容集以外成立的性质的理论，本文概述了了这一理论的发展和现状。