Description Usage Arguments Details Value See Also Examples
Solve a matrix using iterative methods.
1 2 3 4 5 | jacobi(A, b, tol = 1e-06, maxiter = 100)
gaussseidel(A, b, tol = 1e-06, maxiter = 100)
cgmmatrix(A, b, tol = 1e-06, maxiter = 100)
|
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 |
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.
the solution vector
Other linear:
choleskymatrix()
,
detmatrix()
,
gdls()
,
invmatrix()
,
lumatrix()
,
refmatrix()
,
rowops
,
tridiagmatrix()
,
vecnorm()
1 2 3 |
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