Fast QR decomposition of HODLR matrices
Provides a stable and efficient QR decomposition for hierarchical low-rank matrices, addressing numerical instability issues in existing methods.
The authors propose a numerically robust QR decomposition algorithm for HODLR matrices that uses Householder reflectors and compact WY representations, achieving orthogonality down to roundoff error even for highly ill-conditioned matrices.
The efficient and accurate QR decomposition for matrices with hierarchical low-rank structures, such as HODLR and hierarchical matrices, has been challenging. Existing structure-exploiting algorithms are prone to numerical instability as they proceed indirectly, via Cholesky decompositions or a block Gram-Schmidt procedure. For a highly ill-conditioned matrix, such approaches either break down in finite-precision arithmetic or result in significant loss of orthogonality. Although these issues can sometimes be addressed by regularization and iterative refinement, it would be more desirable to have an algorithm that avoids these detours and is numerically robust to ill-conditioning. In this work, we propose such an algorithm for HODLR matrices. It achieves accuracy by utilizing Householder reflectors. It achieves efficiency by utilizing fast operations in the HODLR format in combination with compact WY representations and the recursive QR decomposition by Elmroth and Gustavson. Numerical experiments demonstrate that our newly proposed algorithm is robust to ill-conditioning and capable of achieving numerical orthogonality down to the level of roundoff error.