A note on Asymptotic mean-square stability of stochastic linear two-step methods for SDEs
Provides theoretical stability analysis for stochastic two-step methods, which is important for practitioners solving SDEs, but the results are incremental as they extend known deterministic stability concepts to the stochastic setting.
This note derives necessary and sufficient conditions for asymptotic mean-square stability of stochastic linear two-step methods for SDEs, showing that BDF2 is stable for any step-size while AB2 and AM2 are unconditionally stable. Numerical experiments confirm the theoretical findings.
In this note we study the asymptotic mean-square stability for two-step schemes applied to a scalar stochastic differential equation (sde) and applied to systems of sdes. We derive necessary and sufficient conditions for the asymptotic MS-stability of the methods in terms of the parameters of the schemes. The stochastic Backward Differentiation Formula (BDF2) scheme is asymptotically mean-square stable for any step-size whereas the two-step Adams-Bashforth (AB2) and Adams-Moulton (AM2) methods are unconditionally stable. The improved versions of the schemes do not perform better w.r.t their stability behavior in the scalar case, as expected, but the situation is different in more dimensions. Numerical experiments confirm theoretical results.