Performance of algebraic multigrid methods for non-symmetric matrices arising in particle methods

TL;DR

This paper evaluates algebraic multigrid methods for large, sparse, non-symmetric matrices from particle-based fluid flow simulations, highlighting the importance of M-matrix structures and optimal sparsity for convergence and efficiency.

Contribution

It demonstrates how linear optimization can produce M-matrices for meshfree discretizations, improving AMG solver performance for non-symmetric systems.

Findings

M-matrix structure is not always necessary for AMG convergence.

Linear optimization yields optimally sparse M-matrices.

Matrices from the optimization approach lead to faster solutions.

Abstract

Large linear systems with sparse, non-symmetric matrices arise in the modeling of Markov chains or in the discretization of convection-diffusion problems. Due to their potential to solve sparse linear systems with an effort that is linear in the number of unknowns, algebraic multigrid (AMG) methods are of fundamental interest for such systems. For symmetric positive definite matrices, fundamental theoretical convergence results are established, and efficient AMG solvers have been developed. In contrast, for non-symmetric matrices, theoretical convergence results have been provided only recently. A property that is sufficient for convergence is that the matrix be an M-matrix. In this paper, we present how the simulation of incompressible fluid flows with particle methods leads to large linear systems with sparse, non-symmetric matrices. In each time step, the Poisson equation is approximated by meshfree finite differences. While traditional least squares approaches do not guarantee an M-matrix structure, an approach based on linear optimization yields optimally sparse M-matrices. For both types of discretization approaches, we investigate the performance of a classical AMG method, as well as an AMLI type method. While in the considered test problems, the M-matrix structure turns out not to be necessary for the convergence of AMG, problems can occur when it is violated. In addition, the matrices obtained by the linear optimization approach result in fast solution times due to their optimal sparsity.