arXiv subjects

Liangying Chen

Publications and source records attributed to Liangying Chen.

3 recordsLinked to original sources

arXiv2026

Transposition Approach to Optimal Control of McKean-Vlasov SPDEs

In this paper, we investigate an optimal control problem for McKean-Vlasov stochastic partial differential equations, in which the coefficients depend on the law of the state process. For systems with nonconvex control sets, we establish a Pontryagin-type stochastic maximum principle that provides necessary optimality conditions for admissible controls. The analysis is based on the classical spike variation method together with the introduction of an adjoint backward stochastic partial differential equation involving Lions derivatives with respect to probability measures. Our results extend the stochastic maximum principle for McKean-Vlasov controlled stochastic differential equations to the infinite-dimensional SPDE setting.

math.PR↗

arXiv2022

Stochastic Verification Theorem for Infinite Dimensional Stochastic Control Systems

The verification theorem serving as an optimality condition for the optimal control problem, has been expected and studied for a long time. The purpose of this paper is to establish this theorem for control systems governed by stochastic evolution equations in infinite dimensions, in which both the drift and the diffusion terms depend on the controls.

math.OC↗

arXiv2021

Relationships Between the Maximum Principle and Dynamic Programming for Infinite Dimensional Stochastic Control Systems

Pontryagin type maximum principle and Bellman's dynamic programming principle serve as two of the most important tools in solving optimal control problems. There is a huge literature on the study of relationship between them. The main purpose of this paper is to investigate the relationships between Pontryagin type maximum principle and dynamic programming principle for control systems governed by stochastic evolution equations in infinite dimensional space, with the control variables appearing into both the drift and the diffusion terms. To do so, we first establish dynamic programming principle for those systems without employing the martingale solutions. Then we establish the desired relationships in both cases that value function associated is smooth and nonsmooth. For the nonsmooth case, in particular, by employing the relaxed transposition solution, we discover the connection between the superdifferentials and subdifferentials of value function and the first-order and second-order adjoint equations.

math.OC↗