Cooling down stochastic differential equations: almost sure convergence

Understand

We consider almost sure convergence of the SDE $dX_t=\alpha_t d t + \beta_t d W_t$ under the existence of a $C^2$-Lyapunov function $F:\mathbb R^d \to \mathbb R$.

• More explicitly, we show that on the event that the process stays local we have almost sure convergence in the Lyapunov function $(F(X_t))$ as well as $\nabla F(X_t)\to 0$, if $|\beta_t|=\mathcal O( t^{-\beta})$ for a $\beta>1/2$.
• If, additionally, one assumes that $F$ is a Lojasiewicz function, we get almost sure convergence of the process itself, given that $|\beta_t|=\mathcal O(t^{-\beta})$ for a $\beta>1$.
• The assumptions are shown to be optimal in the sense that there is a divergent counterexample where $|\beta_t|$ is of order $t^{-1}$.