Numerical methods for stochastic differential equations based on Gaussian mixture

Abstract

We develop in this work a numerical method for stochastic differential equations (SDEs) with weak second order accuracy based on Gaussian mixture. Unlike the conventional higher order schemes for SDEs based on Itô-Taylor expansion and iterated Itô integrals, the proposed scheme approximates the probability measure $\mu (X^{n+1}|X^n=x_n)$ by a mixture of Gaussians. The solution at next time step $X^{n+1}$ is then drawn from the Gaussian mixture with complexity linear in the dimension $d$. This provides a new general strategy to construct efficient high weak order numerical schemes for SDEs.

to appear in Communications of Mathematical Sciences

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