# iterativematrix: Solve a matrix using iterative methods In cmna: Computational Methods for Numerical Analysis

## Description

Solve a matrix using iterative methods.

## Usage

 ```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) ```

## 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

Other linear: `choleskymatrix`, `detmatrix`, `gdls`, `invmatrix`, `lumatrix`, `refmatrix`, `rowops`, `tridiagmatrix`, `vecnorm`

## Examples

 ```1 2 3``` ```A <- matrix(c(5, 2, 1, 2, 7, 3, 3, 4, 8), 3) b <- c(40, 39, 55) jacobi(A, b) ```

cmna documentation built on June 20, 2017, 9:08 a.m.