Optimal Policy Characterization for a Class of Multi-Dimensional Ergodic Singular Stochastic Control Problems
Abstract
In ergodic singular stochastic control problems, a decision-maker can instantaneously adjust the evolution of a state variable using a control of bounded variation, with the goal of minimizing a long-term average cost functional. The cost of control is proportional to the magnitude of adjustments. This paper characterizes the optimal policy and the value in a class of multi-dimensional ergodic singular stochastic control problems. These problems involve a linearly controlled one-dimensional stochastic differential equation, whose coefficients, along with the cost functional to be optimized, depend on a multi-dimensional uncontrolled process Y. We first provide general verification theorems providing an optimal control in terms of a Skorokhod reflection at Y-dependent free boundaries, which emerge from the analysis of an auxiliary Dynkin game. We then fully solve two two-dimensional optimal inventory management problems. To the best of our knowledge, this is the first paper to establish a connection between multi-dimensional ergodic singular stochastic control and optimal stopping, and to exploit this connection to achieve a complete solution in a genuinely two-dimensional setting.
Cite
@article{arxiv.2510.11158,
title = {Optimal Policy Characterization for a Class of Multi-Dimensional Ergodic Singular Stochastic Control Problems},
author = {Alessandro Calvia and Federico Cannerozzi and Giorgio Ferrari},
journal= {arXiv preprint arXiv:2510.11158},
year = {2025}
}