A low-rank approach to the computation of path integrals
It provides an efficient algorithm for high-dimensional path integral computations, which is a known bottleneck in computational physics and finance.
The paper introduces a low-rank method for computing path integrals to solve the reaction-diffusion equation, achieving O(nr M log M + nr^2 M) complexity and O(Mr) memory, where r << n.
We present a method for solving the reaction-diffusion equation with general potential in free space. It is based on the approximation of the Feynman-Kac formula by a sequence of convolutions on sequentially diminishing grids. For computation of the convolutions we propose a fast algorithm based on the low-rank approximation of the Hankel matrices. The algorithm has complexity of $\mathcal{O}(nr M \log M + nr^2 M)$ flops and requires $\mathcal{O}(M r)$ floating-point numbers in memory, where $n$ is the dimension of the integral, $r \ll n$, and $M$ is the mesh size in one dimension. The presented technique can be generalized to the higher-order diffusion processes.