A Parallel Computing Method for the Computation of the Moore–Penrose Generalized Inverse for Shared-Memory Architectures
| datacite.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.contributor.author | Gelvez-Almeida, Elkin | |
| dc.contributor.author | Barrientos, Ricardo | |
| dc.contributor.author | Vilches, Karina | |
| dc.contributor.author | Mora, Marco | |
| dc.date.accessioned | 2025-01-30T14:40:25Z | |
| dc.date.available | 2025-01-30T14:40:25Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | The computation of the Moore–Penrose generalized inverse is a commonly used operation in various fields such as the training of neural networks based on random weights. Therefore, a fast computation of this inverse is important for problems where such neural networks provide a solution. However, due to the growth of databases, the matrices involved have large dimensions, thus requiring a significant amount of processing and execution time. In this paper, we propose a parallel computing method for the computation of the Moore–Penrose generalized inverse of large-size full-rank rectangular matrices. The proposed method employs the Strassen algorithm to compute the inverse of a nonsingular matrix and is implemented on a shared-memory architecture. The results show a significant reduction in computation time, especially for high-rank matrices. Furthermore, in a sequential computing scenario (using a single execution thread), our method achieves a reduced computation time compared with other previously reported algorithms. Consequently, our approach provides a promising solution for the efficient computation of the Moore–Penrose generalized inverse of large-size matrices employed in practical scenarios. | eng |
| dc.format.mimetype | ||
| dc.identifier.citation | E. Gelvez-Almeida, R. J. Barrientos, K. Vilches-Ponce and M. Mora, "A Parallel Computing Method for the Computation of the Moore–Penrose Generalized Inverse for Shared-Memory Architectures," in IEEE Access, vol. 11, pp. 134834-134845, 2023, doi: 10.1109/ACCESS.2023.3338544 | eng |
| dc.identifier.doi | https://doi.org/10.1109/ACCESS.2023.3338544 | |
| dc.identifier.issn | 21693536 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12442/16177 | |
| dc.identifier.url | https://ieeexplore.ieee.org/document/10336814 | |
| dc.language.iso | eng | |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | eng |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | eng |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| dc.source | IEEE Access | eng |
| dc.source | Vol. 11 (2023) | spa |
| dc.subject.keywords | High-performance computing | eng |
| dc.subject.keywords | Moore–Penrose generalized inverse matrix | eng |
| dc.subject.keywords | Neural networks with random weights | eng |
| dc.subject.keywords | Parallel computing | eng |
| dc.subject.keywords | Strassen algorithm | eng |
| dc.title | A Parallel Computing Method for the Computation of the Moore–Penrose Generalized Inverse for Shared-Memory Architectures | eng |
| dc.type.driver | info:eu-repo/semantics/article | |
| dc.type.spa | Artículo científico | |
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| oaire.version | info:eu-repo/semantics/publishedVersion |