| dc.creator | Gelvez-Almeida, Elkin | |
| dc.creator | Barrientos, Ricardo | |
| dc.creator | Vilches-Ponce, Karina | |
| dc.creator | Mora, Marco | |
| dc.date | 2024-01-23T18:21:48Z | |
| dc.date | 2024-01-23T18:21:48Z | |
| dc.date | 2023 | |
| dc.date.accessioned | 2024-05-02T20:32:05Z | |
| dc.date.available | 2024-05-02T20:32:05Z | |
| dc.identifier | http://repositorio.ucm.cl/handle/ucm/5201 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9275386 | |
| dc.description | 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. | |
| dc.language | en | |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | |
| dc.source | IEEE Access, 11, 134834-134845 | |
| dc.subject | Sparse matrices | |
| dc.subject | Parallel processing | |
| dc.subject | Computer architecture | |
| dc.subject | Partitioning algorithms | |
| dc.subject | Matrix decomposition | |
| dc.subject | Symmetric matrices | |
| dc.subject | Computational efficiency | |
| dc.title | A parallel computing method for the computation of the Moore-Penrose generalized inverse for shared-memory architectures | |
| dc.type | Article | |
