Stochastic optimal control problems with measurable coefficients and $L_d$-drift
David Criens

TL;DR
This paper studies stochastic control problems with measurable coefficients, establishing the connection between value functions, PDEs, and semigroups, and demonstrating regularization effects of noise without requiring regularity assumptions.
Contribution
It provides new regularity estimates and a stochastic representation for value functions in control problems with measurable coefficients and $L_d$-drift, extending previous frameworks.
Findings
Value functions are $L_{d_0}$-viscosity solutions to Hamilton-Jacobi-Bellman equations.
The semigroup associated with finite horizon costs regularizes lower semicontinuity to local Hölder continuity.
The semigroup is an $L_{d+1}$-viscosity solution to a related PDE with established regularity estimates.
Abstract
We consider controlled stochastic differential equations (SDEs) with measurable coefficients, a uniformly elliptic diffusion coefficient and an -drift. No space-regularity will be assumed for the coefficients. In this framework we investigate the relation of value functions, partial differential equations (PDEs) and operator semigroups. First, for a cost with infinite time horizon on a bounded domain, we identify the value function as -viscosity solution to a Hamilton-Jacobi-Bellman equation and we establish quantitative regularity estimates. The constant only depends on the space dimension , the ellipticity constants of the diffusion coefficient and the -bound of the drift. To illustrate applications of these results, we provide a uniqueness theorem under an additional assumption on the diffusion coefficient, showing a stochastic representation,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStochastic processes and financial applications · advanced mathematical theories · Mathematical Biology Tumor Growth
