On the pathwise approximation of stochastic differential equations
For researchers in numerical SDEs, this provides a more direct pathwise convergence framework that simplifies analysis and enables adaptive error control, though it is an incremental theoretical advance building on rough path theory.
The paper develops a pathwise convergence theory for stochastic differential equation integrators using rough path theory, requiring only a pathwise bound on truncation errors rather than pth mean convergence. It proves convergence of Euler-Maruyama with fixed and adaptive steps, and demonstrates an adaptive error-control strategy with bounded diffusions.
We consider one-step methods for integrating stochastic differential equations and prove pathwise convergence using ideas from rough path theory. In contrast to alternative theories of pathwise convergence, no knowledge is required of convergence in pth mean and the analysis starts from a pathwise bound on the sum of the truncation errors. We show how the theory is applied to the Euler-Maruyama method with fixed and adaptive time-stepping strategies. The assumption on the truncation errors suggests an error-control strategy and we implement this as an adaptive time-stepping Euler-Maruyama method using bounded diffusions. We prove the adaptive method converges and show some computational experiments.