Memory-efficient compression of $\mathcal{DH}^2$-matrices for high-frequency problems

TL;DR

This paper introduces a novel compression method for weight matrices in high-frequency Helmholtz boundary integral equations, significantly reducing storage needs while maintaining controllable accuracy.

Contribution

It proposes a new compression technique for weight matrices in $ ext{H}^2$-matrices, improving storage efficiency for high-frequency problems.

Findings

Reduces storage requirements for weight matrices

Maintains controllable approximation accuracy

Speeds up solution process for high-frequency problems

Abstract

Directional interpolation is a fast and efficient compression technique for high-frequency Helmholtz boundary integral equations, but it requires a very large amount of storage in its original form. Algebraic recompression can significantly reduce the storage requirements and speed up the solution process accordingly. During the recompression process, weight matrices are required to correctly measure the influence of different basis vectors on the final result, and for highly accurate approximations, these weight matrices require more storage than the final compressed matrix. We present a compression method for the weight matrices and demonstrate that it introduces only a controllable error to the overall approximation. Numerical experiments show that the new method leads to a significant reduction in storage requirements.