| iterativematrix {cmna} | R Documentation |
Solve a matrix using iterative methods
Description
Solve a matrix using iterative methods.
Usage
jacobi(A, b, tol = 1e-06, maxiter = 100)
gaussseidel(A, b, tol = 1e-06, maxiter = 100)
cgmmatrix(A, b, tol = 1e-06, maxiter = 100)
Arguments
A |
a square matrix representing the coefficients of a linear system |
b |
a vector representing the right-hand side of the linear system |
tol |
is a number representing the error tolerence |
maxiter |
is the maximum number of iterations |
Details
jacobi finds the solution using Jacobi iteration.
Jacobi iteration depends on the matrix being diagonally-dominate.
The tolerence is specified the norm of the solution vector.
gaussseidel finds the solution using Gauss-Seidel iteration.
Gauss-Seidel iteration depends on the matrix being either
diagonally-dominate or symmetric and positive definite.
cgmmatrix finds the solution using the conjugate gradient
method. The conjugate gradient method depends on the matrix being
symmetric and positive definite.
Value
the solution vector
See Also
Other linear:
choleskymatrix(),
detmatrix(),
gdls(),
invmatrix(),
lumatrix(),
refmatrix(),
rowops,
tridiagmatrix(),
vecnorm()
Examples
A <- matrix(c(5, 2, 1, 2, 7, 3, 3, 4, 8), 3)
b <- c(40, 39, 55)
jacobi(A, b)