The inverse fast multipole method: using a fast approximate direct solver as a preconditioner for dense linear systems

TL;DR

This paper introduces a linear-scaling preconditioner for dense linear systems based on an approximate direct solve using the inverse fast multipole method, effective for large matrices with smooth kernels.

Contribution

It develops a novel $ ext{O}(N)$ preconditioner for dense matrices leveraging $ ext{H}^2$-matrix structure and low-rank approximations, applicable to large-scale problems.

Findings

Achieves linear computational complexity with controlled accuracy.

Demonstrates effectiveness as a preconditioner in numerical experiments.

Maintains sparsity and efficiency through low-rank fill-in approximations.

Abstract

Although some preconditioners are available for solving dense linear systems, there are still many matrices for which preconditioners are lacking, in particular in cases where the size of the matrix $N$ becomes very large. There remains hence a great need to develop general purpose preconditioners whose cost scales well with the matrix size $N$. In this paper, we propose a preconditioner with broad applicability and with cost $\mathcal{O}(N)$ for dense matrices, when the matrix is given by a smooth kernel. Extending the method using the same framework to general $\mathcal{H}^2$-matrices is relatively straightforward. These preconditioners have a controlled accuracy (machine accuracy can be achieved if needed) and scale linearly with $N$. They are based on an approximate direct solve of the system. The linear scaling of the algorithm is achieved by means of two key ideas. First, the $\mathcal{H}^2$-structure of the dense matrix is exploited to obtain an extended sparse system of equations. Second, fill-ins arising when performing the elimination are compressed as low-rank matrices if they correspond to well-separated interactions. This ensures that the sparsity pattern of the extended sparse matrix is preserved throughout the elimination, hence resulting in a very efficient algorithm with $\mathcal{O}(N \log(1/\varepsilon)^2 )$ computational cost and $\mathcal{O}(N \log 1/\varepsilon )$ memory requirement, for an error tolerance $0 < \varepsilon < 1$. The solver is inexact, although the error can be controlled and made as small as needed. These solvers are related to ILU in the sense that the fill-in is controlled. However, in ILU, most of the fill-in is simply discarded whereas here it is approximated using low-rank blocks, with a prescribed tolerance. Numerical examples are discussed to demonstrate the linear scaling of the method and to illustrate its effectiveness as a preconditioner.