Deterministic control of randomly-terminated processes

We consider both discrete and continuous "uncertain horizon" deterministic control processes, for which the termination time is a random variable. We examine the dynamic programming equations for the value function of such processes, explore their connections to infinite-horizon and optimal-stopping problems, and derive sufficient conditions for the applicability of non-iterative (label-setting) methods. In the continuous case, the resulting PDE has a free boundary, on which all characteristic curves originate. The causal properties of "uncertain horizon" problems can be exploited to design efficient numerical algorithms: we derive causal semi-Lagrangian and Eulerian discretizations for the isotropic randomly-terminated problems, and use them to build a modified version of the Fast Marching Method. We illustrate our approach using numerical examples from optimal idle-time processing and expected response-time minimization.