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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