# Analysis of numerical methods for spectral fractional elliptic equations based on the best uniform rational approximation

## TL;DR

This paper introduces an efficient rational approximation method for solving fractional elliptic equations, demonstrating exponential convergence, positivity preservation, and competitive numerical performance in multiple dimensions.

## Contribution

The paper develops a novel rational approximation approach based on BURA for fractional elliptic problems, with proven convergence and practical advantages over existing methods.

## Key findings

- Exponential convergence of the method with respect to the degree k.
- Positivity preservation under certain discretization conditions.
- Numerical experiments show improved efficiency and accuracy compared to prior methods.

## Abstract

Here we study theoretically and compare experimentally an efficient method for solving systems of algebraic equations, where the matrix comes from the discretization of a fractional diffusion operator. More specifically, we focus on matrices obtained from finite difference or finite element approximation of second order elliptic problems in $\mathbb R^d$, $d=1,2,3$. The proposed methods are based on the best uniform rational approximation (BURA) $r_{\alpha,k}(t)$ of $t^{\alpha}$ on $[0,1]$. Here $r_{\alpha,k}$ is a rational function of $t$ involving numerator and denominator polynomials of degree at most $k$. We show that the proposed method is exponentially convergent with respect to $k$ and has some attractive properties. First, it reduces the solution of the nonlocal system to solution of $k$ systems with matrix $(A +c_j I)$ and $c_j>0$, $j=1,2,\ldots,k$. Thus, good computational complexity can be achieved if fast solvers are available for such systems. Second, the original problem and its rational approximation in the finite difference case are positivity preserving. In the finite element case, positivity preserving results when mass lumping is employed under some mild conditions on the mesh. Further, we prove that in the mass lumping case, the scheme still leads to the expected rate of convergence, at times assuming additional regularity on the right hand side. Finally, we present comprehensive numerical experiments on a number of model problems for various $\alpha$ in one and two spatial dimensions. These illustrate the computational behavior of the proposed method and compare its accuracy and efficiency with that of other methods developed by Harizanov et. al. and Bonito and Pasciak.