Recursive sparse LU decomposition based on nested dissection and low rank approximations

TL;DR

This paper introduces a recursive sparse LU decomposition method for large sparse matrices from PDE discretizations, combining nested dissection, low rank approximations, and randomized algorithms to achieve an efficient $ ext{O}(N)$ solver.

Contribution

It presents a novel recursive LU decomposition technique that leverages nested dissection and low rank approximations for efficient PDE solvers, with proven linear complexity.

Findings

Achieves $ ext{O}(N)$ complexity under mild assumptions.

Effective in both symmetric and non-symmetric cases.

Numerical experiments confirm efficiency and accuracy.

Abstract

When solving partial differential equations (PDEs) using finite difference or finite element methods, efficient solvers are required for handling large sparse linear systems. In this paper, a recursive sparse LU decomposition for matrices arising from the discretization of linear PDEs is proposed based on the nested dissection and low rank approximations. The matrix is reorganized based on the nested structure of the associated graph. After eliminating the interior vertices at the finest level, dense blocks on the separators are hierarchically sparsified using low rank approximations. To efficiently skeletonize these dense blocks, we split the separators into segments and introduce a hybrid algorithm to extract the low rank structures based on a randomized algorithm and the fast multipole method. The resulting decomposition yields a fast direct solver for sparse matrices, applicable to both symmetric and non-symmetric cases. Under a mild assumption on the compression rate of dense blocks, we prove an $\O(N)$ complexity for the fast direct solver. Several numerical experiments are provided to verify the effectiveness of the proposed method.