1. A parallel hierarchical blocked adaptive cross approximation algorithm.
- Author
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Liu, Yang, Sid-Lakhdar, Wissam, Rebrova, Elizaveta, Ghysels, Pieter, and Li, Xiaoye Sherry
- Subjects
SINGULAR value decomposition ,COMPUTATIONAL complexity - Abstract
This article presents a low-rank decomposition algorithm based on subsampling of matrix entries. The proposed algorithm first computes rank-revealing decompositions of submatrices with a blocked adaptive cross approximation (BACA) algorithm, and then applies a hierarchical merge operation via truncated singular value decompositions (H-BACA). The proposed algorithm significantly improves the convergence of the baseline ACA algorithm and achieves reduced computational complexity compared to the traditional decompositions such as rank-revealing QR. Numerical results demonstrate the efficiency, accuracy, and parallel scalability of the proposed algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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