Efficient spectral sparse grid approximations for solving multi-dimensional forward backward SDEs

TL;DR

This paper introduces a spectral sparse grid method for efficiently solving high-dimensional forward-backward stochastic differential equations, improving accuracy and computational speed using advanced quadrature and interpolation techniques.

Contribution

It extends previous work by integrating spectral sparse grid approximations with Gaussian-Hermite quadrature for high-dimensional FBSDEs, enhancing efficiency and accuracy.

Findings

Achieved high accuracy in high-dimensional FBSDE solutions

Demonstrated computational efficiency with FFT acceleration

Validated methods through multiple numerical examples

Abstract

This is the second part in a series of papers on multi-step schemes for solving coupled forward backward stochastic differential equations (FBSDEs). We extend the basic idea in our former paper [W. Zhao, Y. Fu and T. Zhou, SIAM J. Sci. Comput., 36 (2014), pp. A1731-A1751] to solve high-dimensional FBSDEs, by using the spectral sparse grid approximations. The main issue for solving high dimensional FBSDEs is to build an efficient spatial discretization, and deal with the related high dimensional conditional expectations and interpolations. In this work, we propose the sparse grid spatial discretization. We use the sparse grid Gaussian-Hermite quadrature rule to approximate the conditional expectations. And for the associated high dimensional interpolations, we adopt an spectral expansion of functions in polynomial spaces with respect to the spatial variables, and use the sparse grid approximations to recover the expansion coefficients. The FFT algorithm is used to speed up the recovery procedure, and the entire algorithm admits efficient and high accurate approximations in high-dimensions, provided that the solutions are sufficiently smooth. Several numerical examples are presented to demonstrate the efficiency of the proposed methods.