Stochastic theta methods for random periodic solution of stochastic differential equations under non-globally Lipschitz conditions

This work focuses on the numerical approximations of random periodic solutions of stochastic differential equations (SDEs). Under non-globally Lipschitz conditions, we prove the existence and uniqueness of random periodic solutions for the considered equations and its numerical approximations generated by the stochastic theta (ST) methods with \(\theta \in (1/2,1]\). It is shown that the random periodic solution of each ST method converges strongly in the mean square sense to that of SDEs. More precisely, the mean square convergence order is 1/2 for SDEs with multiplicative noise and 1 for SDEs with additive noise, respectively. Numerical results are finally reported to confirm these theoretical findings.