High order fast algorithm for the Caputo fractional derivative

TL;DR

This paper introduces a high-order fast algorithm for computing the Caputo fractional derivative that significantly reduces memory and computational costs while maintaining the same convergence rate as traditional methods.

Contribution

The paper develops a novel high-order fast algorithm for the Caputo fractional derivative with nearly optimal memory usage and computational efficiency, applicable to high-order direct methods.

Findings

Reduces storage from O(n) to O((K+1)log n)

Maintains convergence rate of direct methods

Demonstrates efficiency through numerical examples

Abstract

In the paper, we present a high order fast algorithm with almost optimum memory for the Caputo fractional derivative, which can be expressed as a convolution of $u'(t)$ with the kernel $(t_n-t)^{-\alpha}$. In the fast algorithm, the interval $[0,t_{n-1}]$ is split into nonuniform subintervals. The number of the subintervals is in the order of $\log n$ at the $n$-th time step. The fractional kernel function is approximated by a polynomial function of $K$-th degree with a uniform absolute error on each subinterval. We save $K+1$ integrals on each subinterval, which can be written as a convolution of $u'(t)$ with a polynomial base function. As compared with the direct method, the proposed fast algorithm reduces the storage requirement and computational cost from $O(n)$ to $O((K+1)\log n)$ at the $n$-th time step. We prove that the convergence rate of the fast algorithm is the same as the direct method even a high order direct method is considered. The convergence rate and efficiency of the fast algorithm are illustrated via several numerical examples.