2014•arXiv (Cornell University)Open access

Stochastic Maximum Principle for Optimal Control ofPartial Differential Equations Driven by White Noise

Marco Fuhrman, Ying Hu, Gianmario Tessitore

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Abstract

We prove a stochastic maximum principle ofPontryagin's type for the optimal control of a stochastic partial differential equationdriven by white noise in the case when the set of control actions is convex. Particular attention is paid to well-posedness of the adjoint backward stochastic differential equation and the regularity properties of its solution with values in infinite-dimensional spaces.

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We prove a stochastic maximum principle ofPontryagin's type for the optimal control of a stochastic partial differential equationdriven by white noise in the case when the set of control actions is convex. Particular attention is paid to well-posedness of the adjoint backward stochastic differential equation and the regularity properties of its solution with values in infinite-dimensional spaces.

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Available abstract

We prove a stochastic maximum principle ofPontryagin's type for the optimal control of a stochastic partial differential equationdriven by white noise in the case when the set of control actions is convex. Particular attention is paid to well-posedness of the adjoint backward stochastic differential equation and the regularity properties of its solution with values in infinite-dimensional spaces.

Key concepts: White noise, Stochastic partial differential equation, Stochastic differential equation, Mathematics, Stochastic control, Continuous-time stochastic process, Maximum principle, Applied mathematics

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