Backward Reachability Approach to State-Constrained Stochastic Optimal Control Problems for Jump Diffusion Systems
Jun Moon

TL;DR
This paper develops a backward reachability method to analyze state-constrained stochastic optimal control problems for jump diffusion systems, representing the value function as a zero-level set of a continuous auxiliary solution.
Contribution
It introduces a novel approach linking the original value function to an auxiliary HJB equation with unbounded controls, handling discontinuities and singularities.
Findings
Value function characterized as zero-level set of auxiliary solution
Proved existence and uniqueness of the auxiliary HJB solution
Addressed challenges of unbounded controls and Levy measure singularities
Abstract
In this paper, we consider the stochastic optimal control problem for jump diffusion systems with state constraints. In general, the value function of such problems is a discontinuous viscosity solution of the Hamilton-Jacobi-Bellman (HJB) equation, since the regularity cannot be guaranteed at the boundary of the state constraint. By adapting approaches of \cite{Bokanowski_SICON_2016} and the stochastic target theory, we obtain an equivalent representation of the original value function as the backward reachable set. We then show that this backward reachable can be characterized by the zero-level set of the auxiliary value function for the unconstrained stochastic control problem, which includes two additional unbounded controls as a consequence of the martingale representation theorem. We prove that the auxiliary value function is a unique continuous viscosity solution of the…
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Taxonomy
TopicsAdvanced Mathematical Modeling in Engineering · Stochastic processes and financial applications · Advanced Numerical Methods in Computational Mathematics
