New Algebraic Fast Algorithms for $N$-body Problems in Two and Three Dimensions

TL;DR

This paper introduces two new algebraic hierarchical matrix algorithms for fast matrix-vector products in N-body problems, demonstrating improved efficiency and providing a comparative study with existing methods.

Contribution

The paper develops algebraic multilevel hierarchical algorithms based on weak admissibility and nested bases, offering a black-box approach and a comparative analysis with existing methods.

Findings

Algorithms are more efficient than non-nested $ ext{H}$ matrix algorithms.

Numerical results show competitiveness with standard $ ext{H}^2$ matrix algorithms.

C++ implementation is available at https://github.com/riteshkhan/H2weak/.

Abstract

We present two new algebraic multilevel hierarchical matrix algorithms to perform fast matrix-vector product (MVP) for $N$-body problems in $d$ dimensions, namely efficient $\mathcal{H}^2_{*}$ (fully nested algorithm, i.e., $\mathcal{H}^2$ matrix-like algorithm) and $(\mathcal{H}^2 + \mathcal{H})_{*}$ (semi-nested algorithm, i.e., cross of $\mathcal{H}^2$ and $\mathcal{H}$ matrix-like algorithms). The efficient $\mathcal{H}^2_{*}$ and $(\mathcal{H}^2 + \mathcal{H})_{*}$ hierarchical representations are based on our recently introduced weak admissibility condition in higher dimensions, where the admissible clusters are the far-field and the vertex-sharing clusters. Due to the use of nested form of the bases, the proposed hierarchical matrix algorithms are more efficient than the non-nested algorithms ($\mathcal{H}$ matrix algorithms). We rely on purely algebraic low-rank approximation techniques (e.g., ACA and NCA) and develop both algorithms in a black-box fashion. Another noteworthy contribution of this article is that we perform a comparative study of the proposed algorithms with different algebraic (NCA or ACA-based compression) fast MVP algorithms in $2$D and $3$D. The fast algorithms are tested on various kernel matrices and applied to get fast iterative solutions of a dense linear system arising from the discretized integral equations and radial basis function interpolation. Notably, all the algorithms are developed in a similar fashion in $\texttt{C++}$ and tested within the same environment, allowing for meaningful comparisons. The numerical results demonstrate that the proposed algorithms are competitive to the NCA-based standard $\mathcal{H}^2$ matrix algorithm with respect to the memory and time. The C++ implementation of the proposed algorithms is available at https://github.com/riteshkhan/H2weak/.