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DOE OSTI · 1564074

A parallel hierarchical blocked adaptive cross approximation algorithm

Liu, Yang·Sid-Lakhdar, Wissam·Rebrova, Elizaveta·Ghysels, Pieter·Li, Xiaoye Sherry
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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.

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97 MATHEMATICS AND COMPUTINGadaptive cross approximationmulti-level algorithmsparallelizationrank-revealing decompositionsingular value decomposition

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Liu, Yang, Sid-Lakhdar, Wissam, Rebrova, Elizaveta, Ghysels, Pieter, Li, Xiaoye Sherry. 2020-04-22. A parallel hierarchical blocked adaptive cross approximation algorithm. https://doi.org/10.1177/1094342020918305

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