Gelvez-Almeida, ElkinBarrientos, RicardoVilches, KarinaMora, Marco2025-01-302025-01-302023E. 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.2023.333854421693536https://hdl.handle.net/20.500.12442/16177The 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.pdfengAttribution-NonCommercial-NoDerivs 3.0 United Statesinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://doi.org/10.1109/ACCESS.2023.3338544https://ieeexplore.ieee.org/document/10336814High-performance computingMoore–Penrose generalized inverse matrixNeural networks with random weightsParallel computingStrassen algorithm