A strongly convergent numerical scheme from Ensemble Kalman inversion

TL;DR

This paper develops a strongly convergent numerical scheme for a simplified scalar SDE model related to Ensemble Kalman inversion, addressing convergence issues with taming methods in non-globally Lipschitz settings.

Contribution

It introduces a weaker taming approach and proves strong convergence for a simplified model, advancing the numerical analysis of Ensemble Kalman methods.

Findings

Proves strong convergence of the proposed scheme in a simplified scalar SDE model.

Demonstrates the effectiveness of weaker taming methods over standard approaches.

Provides a framework for analyzing convergence in non-globally Lipschitz stochastic systems.

Abstract

The Ensemble Kalman methodology in an inverse problems setting can be viewed as an iterative scheme, which is a weakly tamed discretization scheme for a certain stochastic differential equation (SDE). Assuming a suitable approximation result, dynamical properties of the SDE can be rigorously pulled back via the discrete scheme to the original Ensemble Kalman inversion. The results of this paper make a step towards closing the gap of the missing approximation result by proving a strong convergence result in a simplified model of a scalar stochastic differential equation. We focus here on a toy model with similar properties than the one arising in the context of Ensemble Kalman filter. The proposed model can be interpreted as a single particle filter for a linear map and thus forms the basis for further analysis. The difficulty in the analysis arises from the formally derived limiting SDE with non-globally Lipschitz continuous nonlinearities both in the drift and in the diffusion. Here the standard Euler-Maruyama scheme might fail to provide a strongly convergent numerical scheme and taming is necessary. In contrast to the strong taming usually used, the method presented here provides a weaker form of taming. We present a strong convergence analysis by first proving convergence on a domain of high probability by using a cut-off or localisation, which then leads, combined with bounds on moments for both the SDE and the numerical scheme, by a bootstrapping argument to strong convergence.