On the Preconditioned Delayed Weighted Gradient Method

Autores

DOI:

https://doi.org/10.5540/tcam.2023.024.03.00437

Palavras-chave:

Gradient methods, Convex quadratic optimization, Krylov subspace methods, Preconditioning

Resumo

In this article a preconditioned version of the Delayed Weighted Gradient Method (DWGM) is presented and analyzed.  In addition to the convergence, some nice properties as  the A- orthogonality of the current transformed gradient with all the previous gradient vectors as well as finite convergence are demonstrated. Numerical experimentation is also offered, exposing the benefits of preconditioning.

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Publicado

2023-07-20

Como Citar

Aleixo, R., & Lara Urdaneta, H. (2023). On the Preconditioned Delayed Weighted Gradient Method. Trends in Computational and Applied Mathematics, 24(3), 437–457. https://doi.org/10.5540/tcam.2023.024.03.00437

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Artigo Original