GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems
SIAM Journal on Scientific and Statistical Computing
Implementation of the GMRES method using householder transformations
SIAM Journal on Scientific and Statistical Computing - Telecommunication Programs at U.S. Universities
A theoretical comparison of the Arnoldi and GMRES algorithms
SIAM Journal on Scientific and Statistical Computing
A Hybrid GMRES algorithm for nonsymmetric linear systems
SIAM Journal on Matrix Analysis and Applications
A flexible inner-outer preconditioned GMRES algorithm
SIAM Journal on Scientific Computing
A transpose-free quasi-minimal residual algorithm for non-Hermitian linear systems
SIAM Journal on Scientific Computing
Residual smoothing techniques for iterative methods
SIAM Journal on Scientific Computing
A quasi-minimal residual variant of the Bi-CGSTAB algorithm for nonsymmetric systems
SIAM Journal on Scientific Computing
Relations Between Galerkin and Norm-Minimizing Iterative Methodsfor Solving Linear Systems
SIAM Journal on Matrix Analysis and Applications
GMRES On (Nearly) Singular Systems
SIAM Journal on Matrix Analysis and Applications
Implicitly Restarted GMRES and Arnoldi Methods for Nonsymmetric Systems of Equations
SIAM Journal on Matrix Analysis and Applications
Iterative solution of linear systems in the 20th century
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. III: linear algebra
Tensor-GMRES Method for Large Systems of Nonlinear Equations
SIAM Journal on Optimization
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
A fast implementation for GMRES method
Journal of Computational and Applied Mathematics
Restarted simpler GMRES augmented with harmonic Ritz vectors
Future Generation Computer Systems - Special issue: Selected numerical algorithms
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There are verities of useful Krylov subspace methods to solve nonsymmetric linear system of equations. GMRES is one of the best Krylov solvers with several different variants to solve large sparse linear systems. Any GMRES implementation has some advantages. As the solution of ill-posed problems are important. In this paper, some GMRES variants are discussed and applied to solve these kinds of problems. Residual smoothing techniques are efficient ways to accelerate the convergence speed of some iterative methods like CG variants. At the end of this paper, some residual smoothing techniques are applied for different GMRES methods to test the influence of these techniques on GMRES implementations.