A Parallel Computing Method for the Computation of the Moore-Penrose Generalized Inverse for Shared-Memory Architectures

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...

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Vydáno v:	IEEE access Ročník 11; s. 1
Hlavní autoři:	Gelvez-Almeida, Elkin, Barrientos, Ricardo J., Vilches-Ponce, Karina, Mora, Marco
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Computational efficiency Computer architecture Computer memory Computing time Generalized inverse High-performance computing Matrix decomposition Moore–Penrose generalized inverse matrix Neural networks neural networks with random weights parallel computing Parallel processing Partitioning algorithms Sparse matrices Strassen algorithm Symmetric matrices
ISSN:	2169-3536, 2169-3536
On-line přístup:	Získat plný text
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