Optimal Feedback Controls of Stochastic Linear Quadratic Control Problems in Infinite Dimensions with Random Coefficients

TL;DR

This paper advances the theory of stochastic linear quadratic control in infinite dimensions by relaxing key assumptions, enabling solutions for a broader class of stochastic PDEs such as heat and Stokes equations.

Contribution

It relaxes the $C_0$-group assumption to a contraction semigroup assumption, broadening the applicability to stochastic parabolic PDEs.

Findings

Established equivalence between optimal feedback operator existence and Riccati equation solvability.

Extended the theory to stochastic PDEs like heat and Stokes equations.

Developed new techniques for Riccati equations in infinite dimensions.

Abstract

It is a longstanding unsolved problem to characterize the optimal feedback controls for general linear quadratic optimal control problem of stochastic evolution equation with random coefficients. A solution to this problem is given in [21] under some assumptions which can be verified for interesting concrete models, such as controlled stochastic wave equations, controlled stochastic Schr\"odinger equations, etc. More precisely, the authors establish the equivalence between the existence of optimal feedback operator and the solvability of the corresponding operator-valued, backward stochastic Riccati equations. However, their result cannot cover some important stochastic partial differential equations, such as stochastic heat equations, stochastic stokes equations, etc. A key contribution of the current work is to relax the $C_0$-group assumption of unbounded linear operator $A$ in [21] and using contraction semigroup assumption instead. Therefore, our result can be well applicable in the linear quadratic problem of stochastic parabolic partial differential equations. To this end, we introduce a suitable notion to the aforementioned Riccati equation, and some delicate techniques which are even new in the finite dimensional case.