Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| GMRES× | Método del Gradiente Conjugado× | |
|---|---|---|
| Campo | Métodos numéricos | Métodos numéricos |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1986 | 1952 |
| Autor original≠ | Youcef Saad and Martin H. Schultz | Magnus Hestenes and Eduard Stiefel |
| Tipo≠ | Iterative linear solver for non-symmetric systems | Iterative linear solver |
| Fuente seminal≠ | Saad, Y., & Schultz, M. H. (1986). GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM Journal on Scientific and Statistical Computing, 7(3), 856–869. DOI ↗ | Hestenes, M. R., & Stiefel, E. (1952). Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards, 49(6), 409–436. DOI ↗ |
| Alias≠ | GMRES(m), restarted GMRES, Krylov-GMRES | CG method, Krylov subspace method |
| Relacionados | 1 | 1 |
| Resumen≠ | GMRES (Generalized Minimal Residual) is an iterative method for solving large sparse non-symmetric or nonsymmetric linear systems Ax = b, developed by Saad and Schultz in 1986. It builds an orthonormal Krylov basis using Arnoldi's method and solves a least-squares problem to minimize residual at each iteration. | The Conjugate Gradient (CG) Method is an iterative algorithm for solving large sparse symmetric positive-definite linear systems Ax = b, developed by Hestenes and Stiefel in 1952. It is one of the most widely used iterative solvers in scientific computing because it converges in at most n iterations for an n × n matrix and typically requires far fewer. |
| ScholarGateConjunto de datos ↗ |
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