一类奇异型平稳随机控制问题

本文研究了一个平稳的奇异型随机控制模型,其状态过程为由随机微分方程生成的扩散过程,这个模型实质性地推广了此前的平稳奇异型随机控制模型．     

一类具漂移项的奇异型平稳模型

研究了一类对称的奇异型平稳随机控制模型,在原始模型受控状态过程的基础上添加了飘移因子,并将原始模型中的费用函数推广为较一般的费用函数,求得了与此类问题有关的一个变分不等式组的解,并且给出了最佳控制策略.     

奇异型随机控制中的平稳问题

随机控制中的平稳问题也称平均期望费用问题,笔者曾在[1]中研究了一类脉冲型平稳随机控制问题.本文再研究一类推广的奇异型平稳问题.奇异型随机控制问题最初大约由[2]引进,由于它在宇航及卫星发射等高科技领域有着重要应用(参看[3],Introduc-tion),故以后研究的文献很多,而在[3]中定型为较一般的模型.笔者在[4]中对[3]中的折扣费用模型做了推广,本文则相应对其中的平稳问题进行推广.本文的主要结果已     

带停时的奇异型随机控制问题在金融投资模型中的应用

以随机分析的知识和最优控制理论为基础,讨论了一类带停时的奇异型随机控制的折扣费用问题在金融投资模型中的应用,将该带停时的奇异型随机控制模型的受控状态过程和费用函数结构都推广到了最一般的形式,使该模型的应用范围更加广泛.通过讨论一组相应的变分不等式的解,分别对退化和非退化两种情况给出了此随机控制问题的最优策略,相应得出了投资模型中的最佳决策,并且证明了变分不等式的解即为最优费用函数.与以往不同的是,所得的相关结论应用到了金融投资模型中,从而解决了一类金融投资问题.     

一类具漂移的“跳-停”奇异型随机控制

以随机分析和最优控制理论为基础,讨论了一类带停时的奇异型随机控制问题.在原模型状态过程的基础上添加了漂移因子,并将原模型中的控制费用函数推广为一般的费用函数.在某些条件下,得到"跳一停"策略是其最优控制策略,并给出了"跳一停"策略存在的条件以及控制方法,所得的结论在实际中有较深的应用背景.     

一类平稳的奇异性随机控制问题的研究

§1.引言 关于奇异型随机控制问题,已有很多文章进行研究,如文献[1]—[3]等。在某种意义上来说,文献[2]的结果比文献[1]更为一般。具体描述如下: 设W_t,t≥0为概率空间(Ω,(?),P)上标准Wiener过程。(?)_t为由此过程所生成的上升σ-域族。以(?)表(?)_t适应左连续零初值有限变差过程的全体。对,有正规分解表全变差过程。我们所说的控制全包含在集中。文献[2]研究的平稳模型是:     

随机环境中有界跳幅随机游动常返性暂留性的另一证明

假定环境是平稳遍历的,对具有有限跳幅的随机环境中的随机游动,该文给出了其常返性暂留性的另一证明.Bremont(2002)的文章中,通过计算逃逸概率的方法给出了证明,而该文的证明采用了鞅收敛定理的方法.     

一类停时问题最佳费用函数之解析表达式及其应用

本文用不同于传统的方法，求出一个类停时问题最佳费用函数的解析表达式并构造出了依赖于此函数的最佳停时，此外，本文借助类停时问题的最佳费用函数求出了一类奇异型随机控制的最佳费用函数。     

一类带停时的奇异型随机控制问题的研究

以随机分析的知识和最优控制理论为基础,讨论了一类带停时的奇异型随机控制的折扣费用模型,在原模型的状态过程的基础上添加了漂移因子和扩散因子,并在λ＜δα的情况下讨论了该问题相应的变分方程的解,给出了此随机控制问题的最优策略,即最优控制和最优停时,并且证明了变分方程的解即为最优费用函数.     

一类最优投资随机控制模型的马氏链算法

现代金融经济中的很多问题可以构建成随机控制模型,而随机控制的求解却存在一定的困难.马氏链算法应该是一种有效的求解随机控制问题的数值方法.本文以Claus Munk的工作为基础,针对一类最优投资模型,具体确定了马氏链的转移矩阵并证明其满足算法收敛条件,并用MATLAB语言编成一个程序实现.     

Limit Theorem for Controlled Backward SDEs and Homogenization of Hamilton–Jacobi–Bellman Equations

We prove a convergence theorem for a family of value functions associated with stochastic control problems whose cost functions are defined by backward stochastic differential equations. The limit function is characterized as a viscosity solution to a fully nonlinear partial differential equation of second order. The key assumption we use in our approach is shown to be a necessary and sufficient assumption for the homogenizability of the control problem. The results generalize partially homogenization problems for Hamilton–Jacobi–Bellman equations treated recently by Alvarez and Bardi by viscosity solution methods. In contrast to their approach, we use mainly probabilistic arguments, and discuss a stochastic control interpretation for the limit equation.     

On a class of singular stochastic control problems driven by Lévy noise

A class of singular stochastic control problems whose value functions satisfy an invariance property was studied by Lasry and Lions (2000). They have shown that, within this class, any singular control problem is equivalent to the corresponding standard stochastic control problem. The equivalence is in the sense that their value functions are equal. In this work, we clarify their idea and extend their work to allow Lévy type noise. In addition, for the purpose of application, we apply our result to an optimal trade execution problem studied by Lasry and Lions (2007).     

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     

On the ergodic and the adaptive control of stochastic differential delay systems

Some problems of ergodic control and adaptive control are formulated and solved for stochastic differential delay systems. The existence and the uniqueness of invariant measures that are solutions of the stochastic functional differential equations for these systems are verified. For an ergodic cost criterion, almost optimal controls are constructed. For an unknown system, the invariant measures and the optimal ergodic costs are shown to be continuous functions of the unknown parameters. Almost self-optimizing adaptive controls are feasibly constructed by an approximate certainty equivalence principle.This research was partially supported by NSF Grants ECS-91-02714 and ECS91-13029.     

最优投资组合模型研究

本文研究了在完备金融市场上 ,投资者最优投资组合的随机模型。在模型参数为常系数 ,效用函数为 (0 ,T],B[0 ,T])上的有界可测函数的情形下 ,得出其最大效用值函数是随机控制问题对应的 HJB方程的平滑解 ;最优策略被证明是存在的 ,并用反馈形式给出了最优投资组合策略。     

Approximating Networks and Extended Ritz Method for the Solution of Functional Optimization Problems

Functional optimization problems can be solved analytically only if special assumptions are verified; otherwise, approximations are needed. The approximate method that we propose is based on two steps. First, the decision functions are constrained to take on the structure of linear combinations of basis functions containing free parameters to be optimized (hence, this step can be considered as an extension to the Ritz method, for which fixed basis functions are used). Then, the functional optimization problem can be approximated by nonlinear programming problems. Linear combinations of basis functions are called approximating networks when they benefit from suitable density properties. We term such networks nonlinear (linear) approximating networks if their basis functions contain (do not contain) free parameters. For certain classes of d-variable functions to be approximated, nonlinear approximating networks may require a number of parameters increasing moderately with d, whereas linear approximating networks may be ruled out by the curse of dimensionality. Since the cost functions of the resulting nonlinear programming problems include complex averaging operations, we minimize such functions by stochastic approximation algorithms. As important special cases, we consider stochastic optimal control and estimation problems. Numerical examples show the effectiveness of the method in solving optimization problems stated in high-dimensional settings, involving for instance several tens of state variables.     

A Technique for Stochastic Control Problems with Unbounded Control Set

We describe a change of time technique for stochastic control problems with unbounded control set. We demonstrate the technique on a class of maximization problems that do not have optimal controls. Given such a problem, we introduce an extended problem which has the same value function as the original problem and for which there exist optimal controls that are expressible in simple terms. This device yields a natural sequence of suboptimal controls for the original problem. By this we mean a sequence of controls for which the payoff functions approach the value function.     

Highly Accurate Numerical Schemes for Stochastic Optimal Control via FBSDEs

This work is concerned with numerical schemes for stochastic optimal control problems (SOCPs) by means of forward backward stochastic differential equations (FBSDEs). We first convert the stochastic optimal control problem into an equivalent stochastic optimality system of FBSDEs. Then we design an efficient second order FBSDE solver and an quasi-Newton type optimization solver for the resulting system. It is noticed that our approach admits the second order rate of convergence even when the state equation is approximated by the Euler scheme. Several numerical examples are presented to illustrate the effectiveness and the accuracy of the proposed numerical schemes.     

Monotonicity and bounds for convex stochastic control models

We consider a general convex stochastic control model. Our main interest concerns monotonicity results and bounds for the value functions and for optimal policies. In particular, we show how the value functions depend on the transition kernels and we present conditions for a lower bound of an optimal policy. Our approach is based on convex stochastic orderings of probability measures. We derive several interesting sufficient conditions of these ordering concepts, where we make also use of the Blackwell ordering. The structural results are illustrated by partially observed control models and Bayesian information models.