部分信息的完全耦合正倒向系统的随机最大值原理

本文研究了系统为Brown运动驱动的完全耦合的非线性正倒向随机微分方程的随机最优控制问题．系统要求可允许控制过程适应于标的Brown运动生成的盯域流的一个子盯域流．对于这种部分信息的随机最优控制问题,在控制区域为凸集和控制变量可以进入控制系统正向扩散系数的情形下,证明了最优性的一个充分条件（验证定理）和一个必要条件．     

带Poisson 跳跃的正倒向随机延迟系统递归最优控制问题的最大值原理

本文研究了带Poisson 跳跃的正倒向随机延迟系统的递归最优控制问题. 利用经典的针状变分方法、对偶技术和带Poisson 跳跃的超前倒向随机微分方程的相关结果, 证明了最优控制的最大值原理, 包括了最优控制满足的必要条件和充分条件.     

L\'evy过程驱动的正倒向随机系统的随机最大值原理

研究了由Teugels鞅和与之独立的多维Brown运动共同驱动的正倒向随机控制系统的最优控制问题. 这里Teugels鞅是一列与L\'{e}vy 过程相关的两两强正交的正态鞅 (见Nualart, Schoutens 在2000年的结果). 在允许控制值域为一非空凸闭集假设下, 采用凸变分法和对偶技术获得了最优控制存在所满足的充分和必要条件. 作为应用, 系统研究了线性正倒向随机系统的二次最优控制问题(简记为FBLQ问题), 通过相应的随机哈密顿系统对最优控制 进行了对偶刻画. 这里的随机哈密顿系统是由Teugels鞅和多维Brown运动共同驱动的线性正倒向随机微分方程, 其由状态方程、伴随方程和最优控制的对偶表示共同来构成.     

带有终端限制的平均场正倒向随机时滞控制系统的最大值原理以及在平均场对策中的应用

研究带有时滞和终端状态限制的平均场正倒向随机控制系统的一个最优控制问题.驱动系统的系数依赖于解、解的时滞以及它们的分布.利用Lions导数,终端扰动方法以及Ekeland变分原则,得到了两种随机最大值原理.通过研究一个线性二次问题和一个生产-消费最优选取的平均场对策问题,对这一理论结果进行了阐述说明.     

带跳的耦合正倒向随机微分方程

本文对带跳的耦合正倒向随机微分方程引入了“桥”的概念,证明了如果两个带跳的耦合正倒向随机微分方程被桥连接着,那么它们有相同的唯一可解性.在此基础上,通过桥的构造,得到一些带跳的正倒向随机微分方程的唯一可解性.     

带跳的耦合正倒向随机微分方程

本文对带跳的耦合正倒向随机微分方程引入了"桥"的概念,证明了如果两个带跳的耦合正倒向随机微分方程被桥连接着,那么它们有相同的唯一可解性.在此基础上,通过桥的构造,得到一些带跳的正倒向随机微分方程的唯一可解性.     

正倒向随机微分方程理论及应用

正倒向随机微分方程源于随机控制和金融等问题的研究,反之,方程理论的研究成果在控制、金融等领域也有着重要的应用。基于正向和倒向随机微分方程的理论成果,正倒向随机微分方程的研究在短时间内取得了长足进步。本文将从方程可解性这一角度出发,对正倒向随机微分方程目前取得的成果进行系统的总结与探讨。     

完全耦合的正倒向随机微分方程及相应的偏微分方程系统

本文利用完全耦合的正倒向随机微分方程,对一类耦合了一个代数方程的二阶拟线性抛物型偏微分方程系统,给出概率表示。在适当的假设下,得到这类偏微分方程系统粘性解的存在唯一性结果。     

完全耦合的正倒向随机微分方程及相应的偏微分方程系统

本文利用完全耦合的正倒向随机微分方程,对一类耦合了一个代数方程的二阶拟线性抛物型偏微分方程系统,给出概率表示.在适当的假设下,得到这类偏微分方程系统粘性解的存在唯一性结果.     

正倒向随机微分方程解的比较定理

讨论了正倒向随机微分方程解的比较问题.阐述了正倒向随机微分方程在随机最优控制、现代金融理论中的广泛而深刻的应用, 对于一类正倒向随机微分方程, 利用Ito公式、停时等随机分析方法,通过构造辅助正倒向随机微分方程,得到了正倒向随机微分方程解的比较定理.     

A Global Optimality Principle for Fully Coupled Mean-field Control Systems

This paper concerns a global optimality principle for fully coupled mean-field control systems.Both the first-order and the second-order variational equations are fully coupled mean-field linear FBSDEs. A new linear relation is introduced, with which we successfully decouple the fully coupled first-order variational equations. We give a new second-order expansion of Y^ε that can work well in mean-field framework. Based on this result, the stochastic maximum principle is proved. The com...     

Maximum Principle for Partially-Observed Optimal Control of Fully-Coupled Forward-Backward Stochastic Systems

This paper is concerned with partially-observed optimal control problems for fully-coupled forward-backward stochastic systems. The maximum principle is obtained on the assumption that the forward diffusion coefficient does not contain the control variable and the control domain is not necessarily convex. By a classical spike variational method and a filtering technique, the related adjoint processes are characterized as solutions to forward-backward stochastic differential equations in finite-dimensional spaces. Then, our theoretical result is applied to study a partially-observed linear-quadratic optimal control problem for a fully-coupled forward-backward stochastic system and an explicit observable control variable is given.     

Partially Observed Analytic Systems with Fully Unbounded Actuators and Sensors-FEM Algorithms

Partially observed control systems described by analytic semigroup are considered. Finite-dimensional feedback control based on FEM approximations and accounting for incomplete observations is constructed. It is shown that this feedback control provides uniform stability (in time) of the originally unstable system. The main novel feature of the problem is that both—control and observation operators—are modeled by fully unbounded operators as they frequently arise in modeling of smart sensors and actuators. This contributes to technical difficulties at the level of perturbation theory for analytic semigroups. It is shown that a careful and rather special approximation in the area of support of the unbounded control/observation operators allows to obtain the right stability estimates. Theoretical results are illustrated with several examples of control problems governed by heat and plate equations.     

Stochastic Maximum Principle for Optimal Control of SPDEs

We prove a version of the maximum principle, in the sense of Pontryagin, for the optimal control of a stochastic partial differential equation driven by a finite dimensional Wiener process. The equation is formulated in a semi-abstract form that allows direct applications to a large class of controlled stochastic parabolic equations. We allow for a diffusion coefficient dependent on the control parameter, and the space of control actions is general, so that in particular we need to introduce two adjoint processes. The second adjoint process takes values in a suitable space of operators on L ⁴.     

Sufficient Stochastic Maximum Principle for Discounted Control Problem

In this article, the sufficient Pontryagin’s maximum principle for infinite horizon discounted stochastic control problem is established. The sufficiency is ensured by an additional assumption of concavity of the Hamiltonian function. Throughout the paper, it is assumed that the control domain \(U\) is a convex bounded set and the control may enter the diffusion term of the state equation. The general results are applied to the controlled stochastic logistic equation of population dynamics.     

Forward and Backward Mean-Field Stochastic Partial Differential Equation and Optimal Control

This paper is mainly concerned with the solutions to both forward and backward mean-field stochastic partial differential equation and the corresponding optimal control problem for mean-field stochastic partial differential equation. The authors first prove the continuous dependence theorems of forward and backward mean-field stochastic partial differential equations and show the existence and uniqueness of solutions to them. Then they establish necessary and sufficient optimality conditions of the control problem in the form of Pontryagin''s maximum principles. To illustrate the theoretical results, the authors apply stochastic maximum principles to study the infinite-dimensional linear-quadratic control problem of mean-field type. Further, an application to a Cauchy problem for a controlled stochastic linear PDE of mean-field type is studied.     

A Maximum Principle for Stochastic Control with Partial Information

Abstract

We study the problem of optimal control of a jump diffusion, that is, a process which is the solution of a stochastic differential equation driven by Lévy processes. It is required that the control process is adapted to a given subfiltration of the filtration generated by the underlying Lévy processes. We prove two maximum principles (one sufficient and one necessary) for this type of partial information control. The results are applied to a partial information mean-variance portfolio selection problem in finance.