𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Improvement of the minimal residual method for solving nonsymmetric linear systems

✍ Scribed by Ju-li Zhang; Er-xiong Jiang

Publisher
Chinese Electronic Periodical Services
Year
2007
Tongue
English
Weight
142 KB
Volume
11
Category
Article
ISSN
1007-6417

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

An extension of the conjugate residual m
An extension of the conjugate residual method to nonsymmetric linear systems
✍ T. Sogabe; M. Sugihara; S.-L. Zhang πŸ“‚ Article πŸ“… 2009 πŸ› Elsevier Science 🌐 English βš– 752 KB

algorithm Coupled two-term recurrences a b s t r a c t The Conjugate Gradient (CG) method and the Conjugate Residual (CR) method are Krylov subspace methods for solving symmetric (positive definite) linear systems. To solve nonsymmetric linear systems, the Bi-Conjugate Gradient (Bi-CG) method has b

Can SOR be an efficient method for solvi
Can SOR be an efficient method for solving nonsymmetric linear systems?
✍ Zbigniew I. WoΕΊnicki πŸ“‚ Article πŸ“… 2001 πŸ› Elsevier Science 🌐 English βš– 736 KB

The main aim of this paper is to examine the performance of SOR algorithms for solving linear systems of the type arising from the difference approximation of nonself-adjoint two-dimensional elliptic partial differential equations. A special attention is paid to the development of efficient techniqu

Conjugate gradient and minimal residual
Conjugate gradient and minimal residual methods for solving symmetric indefinite systems
✍ Yu-Ling Lai; Wen-Wei Lin; Dan'l Pierce πŸ“‚ Article πŸ“… 1997 πŸ› Elsevier Science 🌐 English βš– 941 KB

Norm-minimizing-type methods for solving large sparse linear systems with symmetric and indefinite coefficient matrices are considered. The Krylov subspace can be generated by either the Lanczos approach, such as the methods MINRES, GMRES and QMR, or by a conjugate-gradient approach. Here, we propos