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A parallel computing method for the computation of the Moore-Penrose generalized inverse for shared-memory architectures
| dc.contributor.author | Gelvez-Almeida, Elkin | |
| dc.contributor.author | Barrientos, Ricardo | |
| dc.contributor.author | Vilches-Ponce, Karina | |
| dc.contributor.author | Mora, Marco | |
| dc.date.accessioned | 2024-01-23T18:21:48Z | |
| dc.date.available | 2024-01-23T18:21:48Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/5201 | |
| 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. | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | IEEE Access, 11, 134834-134845 | es_CL |
| dc.subject | Sparse matrices | es_CL |
| dc.subject | Parallel processing | es_CL |
| dc.subject | Computer architecture | es_CL |
| dc.subject | Partitioning algorithms | es_CL |
| dc.subject | Matrix decomposition | es_CL |
| dc.subject | Symmetric matrices | es_CL |
| dc.subject | Computational efficiency | es_CL |
| dc.title | A parallel computing method for the computation of the Moore-Penrose generalized inverse for shared-memory architectures | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.facultad | Facultad de Ciencias de la Ingeniería | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.indexacion | Isi | es_CL |
| dc.ucm.uri | ieeexplore.ieee.org/document/10336814 | es_CL |
| dc.ucm.doi | doi.org/10.1109/ACCESS.2023.3338544 | es_CL |
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