Ergodic maximum principle for stochastic systems

TL;DR

This paper develops an ergodic stochastic maximum principle for finite-dimensional controlled dissipative systems, providing necessary and sufficient conditions for optimality using duality and backward SDE techniques.

Contribution

It introduces a novel ergodic SMP framework for dissipative systems, employing duality and infinite horizon backward SDEs for the first time.

Findings

Established necessary and sufficient optimality conditions.

Constructed a dual process via perturbed linearized equations.

Derived a version of the SMP for infinite horizon systems.

Abstract

We present a version of the stochastic maximum principle (SMP) for ergodic control problems. In particular we give necessary (and sufficient) conditions for optimality for controlled dissipative systems in finite dimensions. The strategy we employ is mainly built on duality techniques. We are able to construct a dual process for all positive times via the analysis of a suitable class of perturbed linearized forward equations. We show that such a process is the unique bounded solution to a Backward SDE on infinite horizon from which we can write a version of the SMP.