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A parallel hierarchical blocked adaptive cross approximation algorithm
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DOI：10.1177/1094342020918305.png)
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Abstract
En 中文
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.
Keywords:
Adaptive cross approximation
singular value decomposition
rank-revealing decomposition
parallelization
multilevel algorithms
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