- -

Preconditioners for rank deficient least squares problems

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Preconditioners for rank deficient least squares problems

Mostrar el registro completo del ítem

Cerdán Soriano, JM.; Guerrero, D.; Marín Mateos-Aparicio, J.; Mas Marí, J. (2020). Preconditioners for rank deficient least squares problems. Journal of Computational and Applied Mathematics. 372:1-11. https://doi.org/10.1016/j.cam.2019.112621

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/161853

Ficheros en el ítem

Thumbnail
Nombre: Cerdán;Guerrero;Marín ...
Tamaño: 513.0Kb
Formato: PDF
Descripción: Versión del Autor.
Abrir/Preview
[Cerrado]
Nombre: CerdanGMM20.pdf
Tamaño: 967.4Kb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor

Metadatos del ítem

Título: Preconditioners for rank deficient least squares problems
Autor: Cerdán Soriano, Juana Mercedes Guerrero, D. Marín Mateos-Aparicio, José Mas Marí, José
Entidad UPV: Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada
Fecha difusión:
Resumen:
[EN] In this paper we present a method for computing sparse preconditioners for iteratively solving rank deficient least squares problems (LS) by the LSMR method. The main idea of the method proposed is to update an ...[+]
Palabras clave: Iterative methods , Rank deficient , Sparse linear systems , Preconditioning , Linear least squares problems
Derechos de uso: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Fuente:
Journal of Computational and Applied Mathematics. (issn: 0377-0427 )
DOI: 10.1016/j.cam.2019.112621
Editorial:
Elsevier
Versión del editor: https://doi.org/10.1016/j.cam.2019.112621
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-85669-P/ES/PROBLEMAS MATRICIALES: COMPUTACION, TEORIA Y APLICACIONES/
info:eu-repo/grantAgreement/AEI//MTM2017-90682-REDT/ES/RED TEMATICA DE ALGEBRA LINEAL, ANALISIS MATRICIAL Y APLICACIONES/
Agradecimientos:
This work was supported by the Spanish Ministerio de Economia, Industria y Competitividad, Spain under grants MTM2017-85669-P and MTM2017-90682-REDT.
Tipo: Artículo

References

Paige, C. C., & Saunders, M. A. (1982). LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares. ACM Transactions on Mathematical Software, 8(1), 43-71. doi:10.1145/355984.355989

Paige, C. C., & Saunders, M. A. (1982). Algorithm 583: LSQR: Sparse Linear Equations and Least Squares Problems. ACM Transactions on Mathematical Software, 8(2), 195-209. doi:10.1145/355993.356000

Golub, G., & Kahan, W. (1965). Calculating the Singular Values and Pseudo-Inverse of a Matrix. Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis, 2(2), 205-224. doi:10.1137/0702016 [+]
Paige, C. C., & Saunders, M. A. (1982). LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares. ACM Transactions on Mathematical Software, 8(1), 43-71. doi:10.1145/355984.355989

Paige, C. C., & Saunders, M. A. (1982). Algorithm 583: LSQR: Sparse Linear Equations and Least Squares Problems. ACM Transactions on Mathematical Software, 8(2), 195-209. doi:10.1145/355993.356000

Golub, G., & Kahan, W. (1965). Calculating the Singular Values and Pseudo-Inverse of a Matrix. Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis, 2(2), 205-224. doi:10.1137/0702016

Fong, D. C.-L., & Saunders, M. (2011). LSMR: An Iterative Algorithm for Sparse Least-Squares Problems. SIAM Journal on Scientific Computing, 33(5), 2950-2971. doi:10.1137/10079687x

Scott, J. (2017). On Using Cholesky-Based Factorizations and Regularization for Solving Rank-Deficient Sparse Linear Least-Squares Problems. SIAM Journal on Scientific Computing, 39(4), C319-C339. doi:10.1137/16m1065380

HSL, A collection of Fortran codes for large scale scientific computation. http://www.hsl.rl.ac.uk/.

Li, N., & Saad, Y. (2006). MIQR: A Multilevel Incomplete QR Preconditioner for Large Sparse Least‐Squares Problems. SIAM Journal on Matrix Analysis and Applications, 28(2), 524-550. doi:10.1137/050633032

Benzi, M., & T?ma, M. (2003). A robust incomplete factorization preconditioner for positive definite matrices. Numerical Linear Algebra with Applications, 10(5-6), 385-400. doi:10.1002/nla.320

Hayami, K., Yin, J.-F., & Ito, T. (2010). GMRES Methods for Least Squares Problems. SIAM Journal on Matrix Analysis and Applications, 31(5), 2400-2430. doi:10.1137/070696313

R. Bru, J. Marín, J. Mas, M. Tůma, Preconditioned iterative methods for solving linear least squares problems, SIAM J. Sci. Comput. 36 (4).

Gould, N., & Scott, J. (2017). The State-of-the-Art of Preconditioners for Sparse Linear Least-Squares Problems. ACM Transactions on Mathematical Software, 43(4), 1-35. doi:10.1145/3014057

Cerdán, J., Marín, J., & Mas, J. (2016). Low-rank updates of balanced incomplete factorization preconditioners. Numerical Algorithms, 74(2), 337-370. doi:10.1007/s11075-016-0151-6

Davis, T. A., & Hu, Y. (2011). The university of Florida sparse matrix collection. ACM Transactions on Mathematical Software, 38(1), 1-25. doi:10.1145/2049662.2049663

Pothen, A., & Fan, C.-J. (1990). Computing the block triangular form of a sparse matrix. ACM Transactions on Mathematical Software, 16(4), 303-324. doi:10.1145/98267.98287

Arridge, S. R., Betcke, M. M., & Harhanen, L. (2014). Iterated preconditioned LSQR method for inverse problems on unstructured grids. Inverse Problems, 30(7), 075009. doi:10.1088/0266-5611/30/7/075009

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem