Generalized Minimal Residual Method

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Description

gmres(A,b) attempts to solve the system of linear equations A*x=b for x.

Usage

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    gmres(A, b, x0 = rep(0, length(b)), 
          errtol = 1e-6, kmax = length(b)+1, reorth = 1)

Arguments

A

square matrix.

b

numerical vector or column vector.

x0

initial iterate.

errtol

relative residual reduction factor.

kmax

maximum number of iterations

reorth

reorthogonalization method, see Details.

Details

Iterative method for the numerical solution of a system of linear equations. The method approximates the solution by the vector in a Krylov subspace with minimal residual. The Arnoldi iteration is used to find this vector.

Reorthogonalization method:
1 – Brown/Hindmarsh condition (default)
2 – Never reorthogonalize (not recommended)
3 – Always reorthogonalize (not cheap!)

Value

Returns a list with components x the solution, error the vector of residual norms, and niter the number of iterations.

Author(s)

Based on Matlab code from C. T. Kelley's book, see references.

References

C. T. Kelley (1995). Iterative Methods for Linear and Nonlinear Equations. SIAM, Society for Industrial and Applied Mathematics, Philadelphia, USA.

See Also

solve

Examples

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A <- matrix(c(0.46, 0.60, 0.74, 0.61, 0.85,
              0.56, 0.31, 0.80, 0.94, 0.76,
              0.41, 0.19, 0.15, 0.33, 0.06,
              0.03, 0.92, 0.15, 0.56, 0.08,
              0.09, 0.06, 0.69, 0.42, 0.96), 5, 5)
x <- c(0.1, 0.3, 0.5, 0.7, 0.9)
b <- A %*% x
gmres(A, b)
# $x
#      [,1]
# [1,]  0.1
# [2,]  0.3
# [3,]  0.5
# [4,]  0.7
# [5,]  0.9
# 
# $error
# [1] 2.37446e+00 1.49173e-01 1.22147e-01 1.39901e-02 1.37817e-02 2.81713e-31
# 
# $niter
# [1] 5

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